Snapshot — 2026-07

2026-07-01 · 236 employees

Synthesis Software Technologies · Technology Office · Snapshot 2026-07-01 · long-term trends →

74%
License adoption
174 of 236 employees
62
Employees with no tools
26% of org — priority activation
42%
Projects using AI
21 of 50 projects (45 active)
293
Total license seats
across 6 tools

Key insights

Headline takeaways auto-derived from this snapshot — counts of high-/low-adoption BUs, dominant tools, and activation gaps. The aim is to surface what ExCo would otherwise pull out of the report by hand.

🟢
7 BUs at 100% adoption
Sales, Professional Services, Finance… fully equipped.
🟡
10 BUs at 50–79% adoption
Capacity to close quickly with targeted activation.
🔴
1 BUs need activation
1 BUs at 0%: Marketing.
🤖
Claude is most-used (53%)
124 of 236 employees licensed. Niche tools (<15%): Cursor, Gemini, Microsoft Copilot.
📊
73.7% overall — ahead of PS industry median (~55–60%)
Licensed = at least one AI tool seat resolved to the employee.
🎯
62 employees (26%) have zero tools
Priority activation cohort for next month.

License allocation by tool

Tool Licensed % workforce Prior month Δ
ChatGPT 66 28.0% 82 ▼ 16
Claude 124 52.5% 99 ▲ 24
Cursor 17 7.2% 20 ▼ 3
Gemini 20 8.5% 20 ±0
GitHub Copilot 0 0.0% 0 ±0
GitHub Copilot (new export) 43 18.2% 0 ▲ 42
Microsoft Copilot 23 9.7% 15 ▲ 8

Counts reflect resolved licenses only — 3 unmatched rows are excluded (see Data quality below). Δ compares to the prior snapshot loaded into data/history/.

License allocation details and cost reports

Person-level allocation table joined through employee_code where available. Cost remains in the generated Excel workbook when that artifact is present in the private data repo.

Tool Person Business Unit Employee Code Seat Tier Usage Match
ChatGPT Amy Pegram Regtech amy — — email
ChatGPT Andries Steenkamp Halo andries — — email
ChatGPT Archana Arakkal Technology archana — — email
ChatGPT Arielle Horwitz Halo arielle — — email
ChatGPT Arno Strydom Regtech arno — — email
ChatGPT Barkley van Wyngaard Code barkley.vanwyngaard — — email
ChatGPT Brad Sacks Araxi brad — — name
ChatGPT Byron Phillips Managed Operations byron — — email
ChatGPT Chad Epstein Cloud chad — — email
ChatGPT Dean Maier Cloud dean — — email
ChatGPT Gareth Corbishley Halo gareth — — email
ChatGPT Harsha Maloo Payment Centre of Excellence harsha — — email
ChatGPT Harshil Sheganlall Regtech harshil — — email
ChatGPT Himesh Deva Business Enablement & Operations himesh — — email
ChatGPT Humaira Ahmed Regtech humaira — — email
ChatGPT Jannes Kruger Code jannes.kruger — — email
ChatGPT Jared Naude Managed Operations jared — — email
ChatGPT Jarryd Deane Code jarryd — — email
ChatGPT Jonathan Lew Intelligent Data jonathanl — — email
ChatGPT Jonathan Jacobs Halo jonathanj — — email
ChatGPT Jonathan Sidney Cloud jonathan — — email
ChatGPT Kgomotso Sito Cryptography kgomotso — — email
ChatGPT Kgotso Phiri Code kgotsop — — email
ChatGPT Kieron Ekron Technology kieron — — email
ChatGPT Lesego Mabe Halo lesego — — email
ChatGPT Lesley van den Heever Sales lesley — — email
ChatGPT Liad Peretz Product Incubation liad — — email
ChatGPT Lufuno Mabirimisa Managed Operations lufuno — — email
ChatGPT Mantombi Ngwenya Finance mantombi — — email
ChatGPT Marcin Wójcik Code marcin — — email
ChatGPT Marcus Loveland Regtech marcus — — email
ChatGPT Marcus Mahlatjie Cloud marcus.mahlatjie — — email
ChatGPT Mark McNaughton Managed Operations mark — — email
ChatGPT Marsh Middleton Sales marsh.middleton — — email
ChatGPT Melissa Kramer Halo melissak — — email
ChatGPT Michael Phoya Regtech michael.p — — email
ChatGPT Michael Shapiro Executive michael — — email
ChatGPT Michelle Esbend Finance michelle — — email
ChatGPT Miguel Laranjeira Code miguel — — email
ChatGPT Michael Grant Product Development Services mikeg — — email
ChatGPT Muhiya Sumba Halo muhiya — — email
ChatGPT Naseem Ahmed Cloud naseem — — email
ChatGPT Nikita Venter Sales nikita — — email
ChatGPT Paul Spagnoletti Sales paul.spagnoletti — — email
ChatGPT Phuti Teffo Managed Operations phuti — — email
ChatGPT Preshalin Naidoo Code preshalin — — email
ChatGPT Rui Felix Cloud rui — — email
ChatGPT Ryan Harris Code ryan — — email
ChatGPT Salvatore Errera Regtech salvatore — — email
ChatGPT Sharika Narsing Regtech sharika — — email
ChatGPT Sharon Andrews Human Resources sharon — — email
ChatGPT Steyn Basson Business Enablement & Operations steyn — — email
ChatGPT Tashia Hillebrand Sales tashia — — email
ChatGPT Tayla Boni PMO tayla — — email
ChatGPT Tendai Musonza Cloud tendaim — — name
ChatGPT Tezlin Wilkinson Finance tezlin — — email
ChatGPT Thulani Kula Managed Operations thulani — — email
ChatGPT Velaphi Libilo Managed Operations velaphi — — email
ChatGPT Vivek Singh Cloud vivek.singh — — email
ChatGPT Yolande Roberts Sales yolande — — email
ChatGPT Amy Mitchell Halo amym — — email
ChatGPT Enoch Chandayengerwa Intelligent Data enoch — — email
ChatGPT Marais Neethling Product Incubation marais — — email
ChatGPT Rodney Ellis Ellis Managed Operations rodney — — email
ChatGPT Tjaard Du Plessis Professional services tjaard — — email
ChatGPT Tom Wells Technology tom — — email
Claude Jeanette Fevrier Code jeanette Standard — email
Claude Mary-Lynn Raath Payment Centre of Excellence mary-lyn.raath Standard — email
Claude Muhiya Sumba Halo muhiya Standard — email
Claude Poornima Sudarshan Payment Centre of Excellence poornima.sudarshan Standard — email
Claude Ajendra Jaggeth Sales ajendra Standard — email
Claude Ahmed Rahimi Code ahmed.rahimi Standard — email
Claude Amber Blignaut Business Enablement & Operations amber Standard — email
Claude Amit Sharma Payment Centre of Excellence amit Standard — email
Claude Angie Church Business Enablement & Operations angie Standard — email
Claude Anna Thomas Professional Services anna.thomas Standard — email
Claude Archana Arakkal Technology archana Standard — email
Claude Arielle Horwitz Halo arielle Standard — email
Claude Arno Strydom Regtech arno Standard — email
Claude Arpit Lahoti Code arpit.lahoti Standard — email
Claude Asher Radowsky Cloud asher Standard — email
Claude Athini Ludidi Managed Operations athini Standard — email
Claude Barkley van Wyngaard Code barkley.vanwyngaard Standard — email
Claude Barry Kruger Araxi barry Standard — email
Claude Brandon Fairweather Professional Services brandon.fairweather Standard — email
Claude Brendan Potgieter Business Enablement & Operations brendan Standard — email
Claude Carly Garmany PMO carly Standard — email
Claude Chase Agulhas Code chase Standard — email
Claude Claire Dann Business Enablement & Operations claire Standard — email
Claude Clara Pensalfine Managed Operations clara Standard — email
Claude Cleorese Manes Cloud cleorese Standard — email
Claude Craig Ngwerume Business Enablement & Operations craign Standard — email
Claude Damien Maier Halo damien Standard — email
Claude Darren Bak Intelligent Data darren Standard — email
Claude David Willis Payment Centre of Excellence davidw Standard — email
Claude Dean Maier Cloud dean Standard — email
Claude Declan FitzPatrick Cloud declan Standard — email
Claude Denieke Van Niekerk Regtech denieke Standard — email
Claude Devin Marder Intelligent Data devin Standard — email
Claude Devina Naidoo Sales devina Standard — email
Claude Devon Theron Halo devont Standard — email
Claude Dino Areias Regtech dino Standard — email
Claude Dirk Steynberg Intelligent Data dirk Standard — email
Claude Donovan Broughton Intelligent Data donovan Standard — email
Claude Duncan Kubayi Intelligent Data duncan Standard — email
Claude Elizabeth Bramley Technology elizabeth.bramley Premium — email
Claude Garth Smith Payment Centre of Excellence garth Standard — email
Claude Harsha Maloo Payment Centre of Excellence harsha Standard — email
Claude Himesh Deva Business Enablement & Operations himesh Standard — email
Claude Ian Weber Intelligent Data ian Standard — email
Claude Jannes Kruger Code jannes.kruger Standard — email
Claude Jared Naude Managed Operations jared Standard — email
Claude Jason Mervis Code jason Premium — email
Claude Jay-Dee Sale Code jay.sale Standard — email
Claude Jayden Hardman Code jayden Standard — email
Claude Jessica-May Bergh PMO jessica-may Standard — email
Claude Jonathan Jacobs Halo jonathanj Standard — email
Claude Jonathan Sidney Cloud jonathan Standard — email
Claude Joshua Warneke Regtech joshuaw Standard — email
Claude Kgotso Phiri Code kgotsop Standard — email
Claude Kate Alcock Halo kate Standard — email
Claude Kieron Ekron Technology kieron Standard — email
Claude Konrad Kolbe Sales konrad Standard — email
Claude Kudzai Muranga Code kudzai Standard — email
Claude Leandre Roux Cloud leandre Standard — email
Claude Lesley van den Heever Sales lesley Standard — email
Claude Lindani Mabaso Code lindanim Standard — email
Claude Louis Mosotho Cryptography louis Standard — email
Claude Louis van der Walt PMO louisvdw Standard — email
Claude Luke Holmwood Code lukeh Standard — email
Claude Marcus Loveland Regtech marcus Standard — email
Claude Marinda Rossouw Regtech marinda Standard — email
Claude Mark McNaughton Managed Operations mark Standard — email
Claude Marsh Middleton Sales marsh.middleton Standard — email
Claude Massimo Predieri Code massimo Premium — email
Claude Matthew Robinson Professional Services matthew.robinson Standard — email
Claude Matthew Crockett Code matthew Standard — email
Claude Melissa Kramer Halo melissak Standard — email
Claude Mia van Sittert Sales mia Standard — email
Claude Michael Phoya Regtech michael.p Standard — email
Claude Michael Shapiro Executive michael Standard — email
Claude Michael Nyakarombo Regtech michaeln Standard — email
Claude Michaela Schormann Code michaela Premium — email
Claude Miguel Laranjeira Code miguel Standard — email
Claude Mikael Daniels Intelligent Data mikael Standard — email
Claude Michael Grant Product Development Services mikeg Premium — email
Claude Neil Adamson Professional Services neil.adamson Standard — email
Claude Neldan Janse Van Rensburg Code neldan Standard — email
Claude Nick Walker Intelligent Data nick Standard — email
Claude Nitesh Dhoogar PMO nitesh Standard — email
Claude Njabulo Mashiane Managed Operations njabulom Standard — email
Claude Paul (unmatched) SST000475 Standard — email
Claude Preshalin Naidoo Code preshalin Standard — email
Claude Prince Luhanga Regtech prince Standard — email
Claude RG Ross Professional services rg Standard — email
Claude Rhuli Nghondzweni Intelligent Data rhuli Standard — email
Claude Rodney Ellis Ellis Managed Operations rodney Standard — email
Claude Rolf Deppe Payment Centre of Excellence rolf Standard — email
Claude Ronnie Mokoena Managed Operations ronnie Standard — email
Claude Rui Felix Cloud rui Standard — email
Claude Ryan Harris Code ryan Standard — email
Claude Salvatore Errera Regtech salvatore Standard — email
Claude Saskia Bester Regtech saskia Standard — email
Claude Sean Aucamp Code sean.aucamp Standard — email
Claude Sharika Narsing Regtech sharika Standard — email
Claude Shaun Victor PMO shaun Standard — email
Claude Sibabalwe Jikani Code sibabalwe Standard — email
Claude Siyabonga Mathebula Cloud siyabonga Standard — email
Claude Steve Mbuguje Intelligent Data steve Standard — email
Claude Steyn Basson Business Enablement & Operations steyn Standard — email
Claude Tripti Pande Payment Centre of Excellence tripti Standard — email
Claude Tamelani Netshilema Regtech tamelani Standard — email
Claude Tammy Nkuna Halo tammy Standard — email
Claude Taona Madawo Cloud taona Standard — email
Claude Tashia Hillebrand Sales tashia Standard — email
Claude Natasha Smith Professional Services natasha.smith Standard — email
Claude Tayla Boni PMO tayla Standard — email
Claude Teveshan Valaitham Regtech teveshan Standard — email
Claude Thabo Ranamane Code thabo Premium — email
Claude Tjaard Du Plessis Professional services tjaard Standard — email
Claude Tom Wells Technology tom Premium — email
Claude Ruben De Beer Code ruben Standard — email
Claude Vivien Baker Sales vivien Standard — email
Claude Werner de Jager Sales werner Standard — email
Claude Wihan van Rensburg Code wihan.vanrensburg Standard — email
Claude Jonathan Lew Intelligent Data jonathanl Standard — email
Claude Zander Rosslee Code zander Standard — email
Claude Shulka Ramlal Code shulka Standard — email
Claude Martin Myburgh Cloud martin.myburgh Standard — email
Claude Terence Palani Code terence Premium — email
Cursor Ahmed Rahimi Code ahmed.rahimi — 20.00/$20+/64.57 email
Cursor Archana Arakkal Technology archana — 0.00/0.00/0.00 email
Cursor Dalya Blecher Code dalya — 5.92/0.00/0.00 email
Cursor Ernst Eksteen Code ernst — 20.00/13.12/0.00 email
Cursor Henko Germishuizen Code henko.germishuizen — 20.00/$20+/33.36 email
Cursor Ivan Williams Cloud ivan.williams — 20.00/1.22/0.00 email
Cursor Jannes Kruger Code jannes.kruger — 20.00/9.65/0.00 email
Cursor Jay-Dee Sale Code jay.sale — 20.00/$20+/24.08 email
Cursor Marcin Wójcik Code marcin — 20.00/$20+/40.13 email
Cursor Martin Myburgh Cloud martin.myburgh — 0.00/0.00/0.00 name
Cursor Wihan van Rensburg Code wihan.vanrensburg — 20.00/$20+/58.48 email
Cursor Himesh Deva Business Enablement & Operations himesh — 0.00/0.00/0.00 email
Cursor Jeanette Fevrier Code jeanette — 20.00/$20+/0.00 email
Cursor Kieron Ekron Technology kieron — 0.00/0.00/0.00 email
Cursor Louis-Philip Shahim Cloud louis-philip — 20.00/3.28/0.00 email
Cursor Rui Felix Cloud rui — 0.00/0.00/0.00 email
Cursor Wian Nell Code wian.nell — 20.00/$20+/0.00 email
Gemini Archana Arakkal Technology archana — — email
Gemini Craig Fuchs Code craigf — — email
Gemini Darren Bak Intelligent Data darren — — email
Gemini Dean Maier Cloud dean — — email
Gemini Dirk Steynberg Intelligent Data dirk — — email
Gemini Duncan Kubayi Intelligent Data duncan — — email
Gemini Enoch Chandayengerwa Intelligent Data enoch — — email
Gemini Harsha Maloo Payment Centre of Excellence harsha — — email
Gemini Ivan Williams Cloud ivan.williams — — email
Gemini Leandre Roux Cloud leandre — — email
Gemini Louis-Philip Shahim Cloud louis-philip — — email
Gemini Marion James Cloud marion — — email
Gemini Mark McNaughton Managed Operations mark — — email
Gemini Marsh Middleton Sales marsh.middleton — — email
Gemini Matthew Crockett Code matthew — — email
Gemini Melissa Kramer Halo melissak — — email
Gemini Niren Subramoney Finance nirens — — email
Gemini RG Ross Professional services rg — — email
Gemini Rui Felix Cloud rui — — email
Gemini Siyabonga Mathebula Cloud siyabonga — — email
GitHub Copilot (new export) Tom Wells Technology tom — — handle
GitHub Copilot (new export) Michael Grant Product Development Services mikeg — — handle
GitHub Copilot (new export) Arno Strydom Regtech arno — — handle
GitHub Copilot (new export) Ruben De Beer Code ruben — — handle
GitHub Copilot (new export) Ryan Harris Code ryan — — handle
GitHub Copilot (new export) Zander Rosslee Code zander — — handle
GitHub Copilot (new export) Liad Peretz Product Incubation liad — — handle
GitHub Copilot (new export) Harshil Sheganlall Regtech harshil — — handle
GitHub Copilot (new export) Massimo Predieri Code massimo — — handle
GitHub Copilot (new export) James Eckhardt Cryptography james — — handle
GitHub Copilot (new export) Denieke Van Niekerk Regtech denieke — — handle
GitHub Copilot (new export) Prince Luhanga Regtech prince — — handle
GitHub Copilot (new export) Joshua Warneke Regtech joshuaw — — handle
GitHub Copilot (new export) Teveshan Valaitham Regtech teveshan — — handle
GitHub Copilot (new export) Dino Areias Regtech dino — — handle
GitHub Copilot (new export) Bhavesh Sooka Intelligent Data bhavesh — — handle
GitHub Copilot (new export) Marcus Mahlatjie Cloud marcus.mahlatjie — — handle
GitHub Copilot (new export) Archana Arakkal Technology archana — — handle
GitHub Copilot (new export) Ahmed Rahimi Code ahmed.rahimi — — handle
GitHub Copilot (new export) Louis-Philip Shahim Cloud louis-philip — — handle
GitHub Copilot (new export) Qiniso19 (unmatched) QV01 — — handle
GitHub Copilot (new export) Doug Geddes Managed Operations doug — — handle
GitHub Copilot (new export) Kyle Fleming Code kyle — — handle
GitHub Copilot (new export) Sean Aucamp Code sean.aucamp — — handle
GitHub Copilot (new export) Amy Pegram Regtech amy — — handle
GitHub Copilot (new export) Jonathan Sidney Cloud jonathan — — handle
GitHub Copilot (new export) Declan FitzPatrick Cloud declan — — handle
GitHub Copilot (new export) Kieron Ekron Technology kieron — — handle
GitHub Copilot (new export) Dirk Steynberg Intelligent Data dirk — — handle
GitHub Copilot (new export) Francois Botha Code francois — — handle
GitHub Copilot (new export) Daniel Schurbohm Code daniels — — handle
GitHub Copilot (new export) Dalya Blecher Code dalya — — handle
GitHub Copilot (new export) Harry Myburgh Intelligent Data harry.myburgh — — handle
GitHub Copilot (new export) Miguel Laranjeira Code miguel — — handle
GitHub Copilot (new export) Asher Radowsky Cloud asher — — handle
GitHub Copilot (new export) Jayden Hardman Code jayden — — handle
GitHub Copilot (new export) Enoch Chandayengerwa Intelligent Data enoch — — handle
GitHub Copilot (new export) Yasheen Bhawanipersad Regtech yasheen — — handle
GitHub Copilot (new export) Mbongeni Ngcobo Managed Operations mbongeni — — handle
GitHub Copilot (new export) Tamelani Netshilema Regtech tamelani — — handle
GitHub Copilot (new export) Michael Phoya Regtech michael.p — — handle
GitHub Copilot (new export) Naseem Ahmed Cloud naseem — — handle
GitHub Copilot (new export) Tjaard Du Plessis Professional services tjaard — — handle
Microsoft Copilot Archana Arakkal Technology archana Microsoft 365 Copilot — email
Microsoft Copilot Barry Kruger Araxi barry Microsoft 365 Copilot — email
Microsoft Copilot Craig Fuchs Code craigf Microsoft 365 Copilot — email
Microsoft Copilot Darren Bak Intelligent Data darren Microsoft 365 Copilot — email
Microsoft Copilot Himesh Deva Business Enablement & Operations himesh Microsoft 365 Copilot — email
Microsoft Copilot Jared Naude Managed Operations jared Microsoft 365 Copilot — email
Microsoft Copilot Kieron Ekron Technology kieron Microsoft 365 Copilot — email
Microsoft Copilot Kovishnee Moodley PMO kovishnee Microsoft 365 Copilot — email
Microsoft Copilot Lesley van den Heever Sales lesley Microsoft 365 Copilot — email
Microsoft Copilot Manthan Kuwadia Finance manthan Microsoft 365 Copilot — email
Microsoft Copilot Marsh Middleton Sales marsh.middleton Microsoft 365 Copilot — email
Microsoft Copilot Michael Grant Product Development Services mikeg Microsoft 365 Copilot — email
Microsoft Copilot Michael Shapiro Executive michael Microsoft 365 Copilot — email
Microsoft Copilot Michelle Esbend Finance michelle Microsoft 365 Copilot — email
Microsoft Copilot Niren Subramoney Finance nirens Microsoft 365 Copilot — email
Microsoft Copilot Paul Spagnoletti Sales paul.spagnoletti Microsoft 365 Copilot — email
Microsoft Copilot Prince Luhanga Regtech prince Microsoft 365 Copilot — email
Microsoft Copilot Steyn Basson Business Enablement & Operations steyn Microsoft 365 Copilot — email
Microsoft Copilot Tashia Hillebrand Sales tashia Microsoft 365 Copilot — email
Microsoft Copilot Tjaard Du Plessis Professional services tjaard Microsoft 365 Copilot — email
Microsoft Copilot Tom Wells Technology tom Microsoft 365 Copilot — email
Microsoft Copilot Vivien Baker Sales vivien Microsoft 365 Copilot — email
Microsoft Copilot Werner de Jager Sales werner Microsoft 365 Copilot — email
Cost report workbook Source folder
AI License Summary - July 2026.xlsx final-output/generated

Adoption by department

Department Headcount With license Adoption
Code 52 38 73%
Cloud 32 18 56%
Regtech 23 18 78%
Halo 22 12 55%
Managed Operations 20 14 70%
Intelligent Data 17 14 82%
Sales 12 12 100%
Payment Centre of Excellence 11 8 73%
PMO 9 7 78%
Business Enablement & Operations 8 7 88%
Professional Services 7 7 100%
Finance 5 5 100%
Technology 4 4 100%
Cryptography 4 3 75%
Araxi 3 2 67%
Product Incubation 2 2 100%
Human Resources 2 1 50%
Marketing 1 0 0%
Product Development Services 1 1 100%
Executive 1 1 100%

Project AI maturity by tier

Each project is assigned to one tier — the highest it qualifies for based on its AI Usage tags. L3 is CI/CD-embedded or full feature in AI; L2 is AI for development; L1 is research-only; L0 is no AI; TBD are projects pending classification.

L3 — CI/CD & Full AI
5 (10%)
L2 — AI for Development
8 (16%)
L1 — Research
8 (16%)
L0 — No AI
13 (27%)
TBD
15 (31%)

Project performance by AI tier

Mean project ratings within each tier. Useful for the “are AI-heavy projects delivering better?” question. Means are over projects with a numeric rating in that field; blank cells mean no scored projects.

Tier Projects Overall Budget Delivery Team CSAT
L3 — CI/CD & Full AI 5 4.66 4.20 4.80 4.80 5.00
L2 — AI for Development 8 4.14 3.62 4.50 4.25 4.62
L1 — Research 8 4.53 4.50 4.25 4.50 4.75
L0 — No AI 13 4.38 3.75 4.50 4.58 4.75
TBD 16 4.86 5.00 4.80 4.83 4.80

AI-using projects by BU

Business Unit Projects Using AI %
Cloud 22 6 27%
Code 14 7 50%
Data 12 8 67%
(no bu) 2 0 0%

Project health

4.46
Avg overall rating
38 scored projects
10
High-care projects
50 projects in tracker
4.53
Avg delivery score
0-5 tracker score
4.56
Avg team score
0-5 tracker score
Project Client Business Unit Lifecycle Active High Care Overall Budget Delivery Team CSAT Scope PIIA Risk / Issue
Absa Branch Server - 2 month extension (Chase and Chad) Absa Cloud Pipeline No No — — — — — — To be assessed —
Absa CIB Application Maturity Project (Chris M) Absa Cloud Execution Yes No 4.50 5.00 4.00 5.00 4.00 4.00 To be assessed To be advised
Absa CIB Application Maturity Project (Hasnain T&M) Absa Cloud Execution Yes No 5.00 5.00 5.00 5.00 5.00 5.00 To be assessed To be advised
Absa CIB Market Suite Mobilisation (AWS Funded) Absa Cloud Execution Yes Yes 4.10 5.00 4.00 3.00 4.00 4.00 All Personal Information/Sensitive Data is managed by the Client Risks/Issues Require Support
Absa CIB Sigma Application Migration - Team Augmentation Absa Cloud Execution Yes No 5.00 5.00 5.00 5.00 5.00 5.00 To be assessed To be advised
Absa Senior PM and Migration Assurance Lead Absa Cloud Execution Yes No 4.85 5.00 4.00 5.00 5.00 5.00 To be assessed To be advised
Model Risk - Viabhav Extension Absa Cloud Execution Yes — 4.80 5.00 5.00 4.00 5.00 5.00 We do not deal with Personal Information/Sensitive Data To be advised
Absa Bank Limited Agentic AI Absa Data Pipeline No No — — — — — — To be assessed —
Absa Bank Limited Agentic AI Absa Data Pipeline No No — — — — — — To be assessed —
Absa CIB - Loan Optimisation AI Use Case Extension (Q1 2026) Absa Data Execution Yes Yes 4.40 3.00 5.00 5.00 5.00 5.00 This project deals with Personal Information/Sensitive Data Risks/Issue Visible & Managed
AlBaraka | Project | Cloud PS | IPSEC Tunnel Planned Change / Service Request 1 Albaraka Cloud Execution Yes No 5.00 5.00 5.00 5.00 5.00 5.00 We do not deal with Personal Information/Sensitive Data To be advised
Tata J36 Engagement Alumni Code Execution Yes Yes 5.00 5.00 5.00 5.00 5.00 5.00 To be assessed To be advised
Avenews GT - Project - Team extension Avenews GT Data Execution Yes No 5.00 5.00 5.00 5.00 5.00 5.00 To be assessed Risks/Issues Not Visible or Managed
AWS MSK/Flink ML Engineer Extension Capitec Data Execution Yes No 4.70 4.00 5.00 5.00 5.00 5.00 To be assessed Risks/Issues Not Visible or Managed
Business Bank Agentic AI Use Case_Bank Statement Agent: Affordability Analysis Capitec Data Pipeline No No — — — — — — To be assessed —
Realtime Fraud Detection_ML Engineering Capitec Data Execution Yes No 4.80 5.00 5.00 4.00 5.00 5.00 To be assessed Risks/Issues Not Visible or Managed
Confident | Project | .NET Framework Upgrades Confident Asset Management Limited (no bu) Execution Yes No — — — — — — To be assessed To be advised
Credeq - Azure Migration Credeq Cloud Execution Yes Yes 3.40 2.00 3.00 4.00 4.00 5.00 To be assessed Risks/Issues Require Support
Credeq Cloudflare Implementation Credeq Cloud Execution Yes No 4.40 3.00 5.00 5.00 5.00 5.00 We do not deal with Personal Information/Sensitive Data Risks/Issues Require Support
Credeq Google Cloud Strategic Agreement Credeq Cloud Execution Yes Yes 4.55 4.00 5.00 5.00 5.00 4.00 This project deals with Personal Information/Sensitive Data Risks/Issue Visible & Managed
Credeq - Guarantee Gateway - Phase 1 Dev Credeq Code Execution Yes No 4.20 3.00 5.00 4.00 5.00 5.00 To be assessed Risks/Issue Visible & Managed
Rapid Data Delivery POC Discovery Insure Code Internal Initiation Yes No — — — — — — To be assessed To be advised
Investec IFB SOW 1 & SOW 2 Investec Bank Code Execution Yes No 4.70 5.00 4.00 5.00 5.00 4.00 We do not deal with Personal Information/Sensitive Data To be advised
Cloudflare Engineering Lombard Cloud Handover Yes No 4.00 3.00 5.00 4.00 4.00 5.00 We do not deal with Personal Information/Sensitive Data Risks/Issue Visible & Managed
Lombard GCP Capacity Upsell on Strat Contract Lombard Cloud Execution Yes No — — — — — — To be assessed To be advised
Lombard Github Migration Lombard Cloud Client Kick Off Yes No — — — — — — To be assessed To be advised
Lombard Google Cloud Strategic Agreement Lombard Cloud Execution Yes No 4.85 5.00 5.00 5.00 5.00 4.00 To be assessed Risks/Issue Visible & Managed
Lombard - CloudM Migration Lombard Code Execution Yes No 5.00 5.00 5.00 5.00 5.00 5.00 To be assessed To be advised
OM Bank Control Tower Old Mutual Bank Cloud Internal Initiation Yes No — 5.00 — — — — To be assessed To be advised
Osiris Business Analyst Osiris Code Execution Yes No 4.80 5.00 5.00 4.00 5.00 5.00 To be assessed To be advised
Multi Contract Consolidation & Extension Osiris Trading Code Execution Yes Yes 3.00 1.00 5.00 3.00 3.00 5.00 We do not deal with Personal Information/Sensitive Data Risks/Issues Not Visible or Managed
Osiris - Scytale - ITGC Automation Software Osiris Trading Code Pipeline No No — — — — — — To be assessed —
PayInc Strat Bucket PayInc Cloud Execution Yes Yes 3.60 1.00 5.00 4.00 5.00 5.00 We do not deal with Personal Information/Sensitive Data Risks/Issues Not Visible or Managed
ShareForce_Build Project ShareForce Code Execution Yes No 5.00 5.00 5.00 5.00 5.00 5.00 To be assessed Risks/Issue Visible & Managed
SBG | Challenger Squad | SOW 1 Standard Bank (no bu) Client Kick Off Yes No — — — 5.00 — — To be assessed To be advised
SBSA - SmartVista Extension and Architecture Review Standard Bank Cloud Execution Yes No 5.00 5.00 5.00 5.00 5.00 5.00 We do not deal with Personal Information/Sensitive Data Risks/Issues Not Visible or Managed
SBSA Business Online+ Extension Standard Bank Cloud Execution Yes No 5.00 5.00 5.00 5.00 5.00 5.00 We do not deal with Personal Information/Sensitive Data To be advised
SBSA | Automation Framework | AI Credit Analyst | CrediAssist POC Standard Bank Cloud Working at Risk Yes Yes 3.50 5.00 2.00 3.00 4.00 2.00 This project deals with Personal Information/Sensitive Data Risks/Issues Require Support
SBSA - Enterprise AI platform - Datahandling -Sage Maker Studio Standard Bank Data Working at Risk Yes Yes 3.65 3.00 3.00 5.00 5.00 2.00 All Personal Information/Sensitive Data is managed by the Client Risks/Issues Require Support
SBSA-AWS Pro-Serve GenAI Enterprise Platform MVP 1 Standard Bank Data Execution Yes No 3.45 1.00 5.00 4.00 5.00 4.00 This project deals with Personal Information/Sensitive Data Risks/Issues Not Visible or Managed
Strate | Team Aug | Confluent Engineering Consulting | Project EXT (2months) Strate Data Execution Yes No 5.00 5.00 5.00 5.00 5.00 5.00 To be assessed Risks/Issue Visible & Managed
Swiffy | New Landing Zone | Old Enviro Migration | Cloud PS Swiffy Cloud Execution Yes No 3.65 1.00 4.00 5.00 5.00 5.00 We do not deal with Personal Information/Sensitive Data Risks/Issue Visible & Managed
University of Cambridge - API Platform team extension University of Cambridge Code Execution Yes No 4.50 5.00 4.00 5.00 4.00 4.00 To be assessed Risks/Issues Require Support
University of Cambridge - B2B Shop lead University of Cambridge Code Execution Yes No 4.70 5.00 4.00 5.00 5.00 4.00 To be assessed Risks/Issues Require Support
University of Cambridge - Design system lead University of Cambridge Code Execution Yes No 4.80 5.00 5.00 4.00 5.00 5.00 We do not deal with Personal Information/Sensitive Data Risks/Issues Require Support
Principal Solution Architect (1 August 2026 - 30 June 2027)) University of Cambridge Data Pipeline Yes No — — — — — — To be assessed To be advised
Principal Solution Architect (11 Oct 2025 - 31 Jul 2026) University of Cambridge Data Execution Yes No 5.00 5.00 5.00 5.00 5.00 5.00 This project deals with Personal Information/Sensitive Data Risks/Issues Not Visible or Managed
Engen Website Maintenance and Support - Extension Vivo Energy PLC Code Execution Yes No 5.00 5.00 5.00 5.00 5.00 5.00 We do not deal with Personal Information/Sensitive Data Risks/Issue Visible & Managed
VEOne Program Squad - Extension Vivo Energy PLC Code Execution Yes No 5.00 5.00 5.00 5.00 5.00 5.00 All Personal Information/Sensitive Data is managed by the Client Risks/Issue Visible & Managed
WFS Control Tower Upgrade Woolworths Financial Services Cloud Execution Yes Yes 2.60 2.00 1.00 3.00 4.00 3.00 We do not deal with Personal Information/Sensitive Data Risks/Issues Require Support

Project AI maturity reports

22
Maturity reports
7 unmatched to tracker
1%
Avg team using AI
10 reports with % populated
0
AI permitted
reports marked yes
0
Data controls
quality controls marked yes
Embedded
11 (50%)
(blank)
6 (27%)
Optimised
2 (9%)
Experimental
2 (9%)
No AI
1 (5%)
Project Client Business Unit Project Type Reporting Period Match AI Permitted % Team Using AI Maturity Where Used Observed Impact
Absa - Loan Optimisation Absa Bank Data Full Delivery 01 April to 30 April crosswalk — — Optimised Development, Delivery, Testing Proof of value is clear, speed, productivity and customer value particularly
Absa - Digital Transformation Migration Absa Bank Cloud Team Augmentation 01 April to 30 April crosswalk — — (blank) — —
April 2026_Team Augmentation_Change Request AV003_Comprehensive Internal Project Health Report Avenues Data Team Augmentation April 2026 crosswalk — 1% Experimental Research, Upskilling and Personal Enablement to compliment delivery. Debugging in unfamiliar domains. Productivity, Speed
May 2026_Team Augmentation_Change Request AV003_Comprehensive Internal Project Health Report Avenues Data Team Augmentation May 2026 crosswalk — 1% Experimental Still the same. Research, Upskilling and Personal Enablement to compliment delivery. Debugging in unfamiliar domains. Productivity, Speed
API PLatform Team Cambridge University (unmatched) Team Augmentation June 2026 unmatched Yes we are working with the client in implementing AI assisted processes in both planning and development 1% Embedded Specs and development So far we could see increase in speed but becuase we did not track previous sprint velocities we dont have anything to compare it to
API PLatform Team Cambridge University (unmatched) Team Augmentation May 2026 unmatched Yes we are working with the client in implementing AI assisted processes in both planning and development 1% Embedded Specs and development So far we could see increase in speed but becuase we did not track previous sprint velocities we dont have anything to compare it to
B2B Shop Lead Cambridge University (unmatched) Team Augmentation May 2026 unmatched — — (blank) — —
B2B Shop Lead Cambridge University (unmatched) Team Augmentation May 2026 unmatched — — (blank) — —
English Design System (EDS) Cambridge University (unmatched) Team Augmentation May 2026 unmatched There is no specific policy at Cambridge that we are aware of but it is encouraged that AI tools be used to get more efficient — Embedded Jira management and Development Faster development
English Design System (EDS) Cambridge University (unmatched) Team Augmentation May 2026 unmatched There is no specific policy at Cambridge that we are aware of but it is encouraged that AI tools be used to get more efficient — Embedded Jira management and Development Faster development
Principal Solutions Architect Cambridge University (unmatched) Team Augmentation June 2026 unmatched — — (blank) — —
Credeq - Data Platform Credeq (Division of Lombard) Cloud Full Delivery April 2026 crosswalk — 1% Embedded General development and research Limited observational impact
Credeq - Guarantee Gateway Credeq (Division of Lombard) Code Full Delivery 2 week sprint cycle crosswalk — — No AI No AI being utilized n/a
Credeq - Guarantee Gateway Credeq (Division of Lombard) Code Full Delivery 2 week sprint cycle crosswalk — — Embedded Fully AI developed Productivity quality
Credeq - Guarantee Gateway Credeq (Division of Lombard) Code Full Delivery 2 week sprint cycle crosswalk Client has no issue with using AI. — Embedded Development (Egnieers) and PM. Faster delivery
Vivo - VEOne Feature Team Extension - Jan - Dec Engen Petroleum (Pty) Limited Code Full Delivery June 2026 crosswalk No specific polisies 1% Embedded Engineering and PM Speed and Quality improvements
Vivo - VEOne Feature Team Extension - Jan - Dec Engen Petroleum (Pty) Limited Code Full Delivery May 2026 crosswalk No specific polisies 1% Embedded Engineering and PM Speed and Quality improvements
Lombard - Cloud M Lombard Insurance Code Full Delivery Monthly crosswalk — 1% Embedded Early but expect Delivery, development, testing Still early
Lombard - GitHub Lombard Insurance Cloud Full Delivery Monthly crosswalk — 1% Embedded Delivery, development, testing Speed of delivery, daily efficiencies, operational efficiencies
Comprehensive Internal Project Health Report_v0.01_ShareForce_June_2026 ShareForce Code Full Delivery Sprint 2&3 crosswalk Yes permitted 1% Optimised Full development used with AI Yes delivery is much faster, the team is ahead of schedule
SBG - CrediAssist POC (Partially AWS Funded) Standard Bank Group Cloud Full Delivery June 2026 crosswalk — — (blank) — —
Comprehensive Internal Project Health Report_v0.011 Strate - Kafka Team Data Team Augmentation — crosswalk — — (blank) — —

Team maturity and wellbeing

Metric Average Reports scored
Morale 4.10 20
Workload 4.65 20
Burnout 4.55 20
Stability 4.55 20
Psych safety 4.85 20
Wellbeing 4.56 20

Wellbeing outliers

Project Client Project Type Reporting Period Wellbeing Burnout Workload Psych Safety
April 2026_Team Augmentation_Change Request AV003_Comprehensive Internal Project Health Report Avenues Team Augmentation April 2026 4.20 4.00 4.00 5.00
May 2026_Team Augmentation_Change Request AV003_Comprehensive Internal Project Health Report Avenues Team Augmentation May 2026 4.20 4.00 4.00 5.00
Credeq - Data Platform Credeq (Division of Lombard) Full Delivery April 2026 4.20 4.00 5.00 4.00
Credeq - Guarantee Gateway Credeq (Division of Lombard) Full Delivery 2 week sprint cycle 4.20 5.00 4.00 5.00
SBG - CrediAssist POC (Partially AWS Funded) Standard Bank Group Full Delivery June 2026 4.20 5.00 5.00 3.00
Credeq - Guarantee Gateway Credeq (Division of Lombard) Full Delivery 2 week sprint cycle 4.40 5.00 5.00 5.00
Absa - Digital Transformation Migration Absa Bank Team Augmentation 01 April to 30 April 4.60 5.00 5.00 5.00
API PLatform Team Cambridge University Team Augmentation May 2026 4.60 5.00 5.00 5.00
B2B Shop Lead Cambridge University Team Augmentation May 2026 4.60 5.00 5.00 5.00
B2B Shop Lead Cambridge University Team Augmentation May 2026 4.60 5.00 5.00 5.00
API PLatform Team Cambridge University Team Augmentation June 2026 4.80 5.00 5.00 5.00
Absa - Loan Optimisation Absa Bank Full Delivery 01 April to 30 April 5.00 5.00 5.00 5.00
Credeq - Guarantee Gateway Credeq (Division of Lombard) Full Delivery 2 week sprint cycle 5.00 5.00 5.00 5.00
Vivo - VEOne Feature Team Extension - Jan - Dec Engen Petroleum (Pty) Limited Full Delivery June 2026 5.00 5.00 5.00 5.00
Vivo - VEOne Feature Team Extension - Jan - Dec Engen Petroleum (Pty) Limited Full Delivery May 2026 5.00 5.00 5.00 5.00
Lombard - Cloud M Lombard Insurance Full Delivery Monthly 5.00 4.00 5.00 5.00
Lombard - GitHub Lombard Insurance Full Delivery Monthly 5.00 4.00 5.00 5.00
Comprehensive Internal Project Health Report_v0.01_ShareForce_June_2026 ShareForce Full Delivery Sprint 2&3 5.00 5.00 5.00 5.00

Compliance posture

3
Sensitive data
reports marked yes
5
PIIA completed
7 reports with status populated
Project Client Project Type Handles Sensitive Data PIIA Completed Match
Absa - Digital Transformation Migration Absa Bank Team Augmentation — — crosswalk
Absa - Loan Optimisation Absa Bank Full Delivery — — crosswalk
April 2026_Team Augmentation_Change Request AV003_Comprehensive Internal Project Health Report Avenues Team Augmentation — — crosswalk
May 2026_Team Augmentation_Change Request AV003_Comprehensive Internal Project Health Report Avenues Team Augmentation — — crosswalk
API PLatform Team Cambridge University Team Augmentation No N/A as yet unmatched
API PLatform Team Cambridge University Team Augmentation No N/A as yet unmatched
B2B Shop Lead Cambridge University Team Augmentation — — unmatched
B2B Shop Lead Cambridge University Team Augmentation — — unmatched
English Design System (EDS) Cambridge University Team Augmentation No Yes unmatched
English Design System (EDS) Cambridge University Team Augmentation No Yes unmatched
Principal Solutions Architect Cambridge University Team Augmentation — — unmatched
Credeq - Data Platform Credeq (Division of Lombard) Full Delivery — — crosswalk
Credeq - Guarantee Gateway Credeq (Division of Lombard) Full Delivery — — crosswalk
Credeq - Guarantee Gateway Credeq (Division of Lombard) Full Delivery — — crosswalk
Credeq - Guarantee Gateway Credeq (Division of Lombard) Full Delivery Yes No crosswalk
Vivo - VEOne Feature Team Extension - Jan - Dec Engen Petroleum (Pty) Limited Full Delivery Yes Yes crosswalk
Vivo - VEOne Feature Team Extension - Jan - Dec Engen Petroleum (Pty) Limited Full Delivery Yes Yes crosswalk
Lombard - Cloud M Lombard Insurance Full Delivery — — crosswalk
Lombard - GitHub Lombard Insurance Full Delivery — — crosswalk
Comprehensive Internal Project Health Report_v0.01_ShareForce_June_2026 ShareForce Full Delivery Yes, it plans to, but not currently No crosswalk
SBG - CrediAssist POC (Partially AWS Funded) Standard Bank Group Full Delivery Yes, we are anticipating the ingestion of sensitive data for this project Yes crosswalk
Comprehensive Internal Project Health Report_v0.011 Strate - Kafka Team Team Augmentation — — crosswalk

Project people bridge

121
Project-person rows
account, project, and tech leads
1
Unmatched people
blank employee_code rows
Role Rows Share
account_manager 51 42.1%
tech_lead 45 37.2%
project_manager 25 20.7%
Project Client Role Person Employee Code Match Method
Absa Bank Limited Agentic AI Absa account_manager Konrad Kolbe konrad name
Absa Bank Limited Agentic AI Absa account_manager Tashia Hillebrand tashia name
Absa Branch Server - 2 month extension (Chase and Chad) Absa account_manager Tashia Hillebrand tashia name
Absa Branch Server - 2 month extension (Chase and Chad) Absa account_manager Tashia Hillebrand tashia name
Absa Branch Server - 2 month extension (Chase and Chad) Absa tech_lead Chad Epstein chad name
Absa CIB - Loan Optimisation AI Use Case Extension (Q1 2026) Absa account_manager Tashia Hillebrand tashia name
Absa CIB - Loan Optimisation AI Use Case Extension (Q1 2026) Absa project_manager Nick Dempster nick.dempster name
Absa CIB - Loan Optimisation AI Use Case Extension (Q1 2026) Absa tech_lead Massimo Predieri massimo name
Absa CIB Application Maturity Project (Chris M) Absa account_manager Tashia Hillebrand tashia name
Absa CIB Application Maturity Project (Chris M) Absa tech_lead Chris Muyaruka chris.muyaruka name
Absa CIB Application Maturity Project (Hasnain T&M) Absa account_manager Tashia Hillebrand tashia name
Absa CIB Application Maturity Project (Hasnain T&M) Absa tech_lead Hasnain Tahir hasnain.tahir name
Absa CIB Market Suite Mobilisation (AWS Funded) Absa account_manager Tashia Hillebrand tashia name
Absa CIB Market Suite Mobilisation (AWS Funded) Absa project_manager Nick Dempster nick.dempster name
Absa CIB Market Suite Mobilisation (AWS Funded) Absa tech_lead Chad Epstein chad name
Absa CIB Sigma Application Migration - Team Augmentation Absa account_manager Tashia Hillebrand tashia name
Absa CIB Sigma Application Migration - Team Augmentation Absa tech_lead Sajjad Khan sajjad.khan name
Absa Senior PM and Migration Assurance Lead Absa account_manager Tashia Hillebrand tashia name
Absa Senior PM and Migration Assurance Lead Absa project_manager Shaun Victor shaun name
Absa Senior PM and Migration Assurance Lead Absa tech_lead Aabhas Sinha aabhas name
Model Risk - Viabhav Extension Absa account_manager Tashia Hillebrand tashia name
Model Risk - Viabhav Extension Absa tech_lead Vaibhav Singh vaibhav.singh name
AlBaraka | Project | Cloud PS | IPSEC Tunnel Planned Change / Service Request 1 Albaraka account_manager Lesley van den Heever lesley name
AlBaraka | Project | Cloud PS | IPSEC Tunnel Planned Change / Service Request 1 Albaraka project_manager Kovishnee Moodley kovishnee name
AlBaraka | Project | Cloud PS | IPSEC Tunnel Planned Change / Service Request 1 Albaraka tech_lead Tendai Musonza tendaim name
Tata J36 Engagement Alumni account_manager Neil Adamson neil.adamson name
Avenews GT - Project - Team extension Avenews GT account_manager Darren Bak darren name
Avenews GT - Project - Team extension Avenews GT tech_lead Zander Rosslee zander name
AWS MSK/Flink ML Engineer Extension Capitec account_manager Mia Van Sittert mia name
AWS MSK/Flink ML Engineer Extension Capitec tech_lead Enoch Chandayengerwa enoch name
Business Bank Agentic AI Use Case_Bank Statement Agent: Affordability Analysis Capitec account_manager Angelique Joubert AJ01 name
Realtime Fraud Detection_ML Engineering Capitec account_manager Mia Van Sittert mia name
Realtime Fraud Detection_ML Engineering Capitec tech_lead Vincent de Comarmond vincentdc name
Confident | Project | .NET Framework Upgrades Confident Asset Management Limited account_manager Lesley van den Heever lesley name
Confident | Project | .NET Framework Upgrades Confident Asset Management Limited project_manager Craig Fuchs craigf name
Confident | Project | .NET Framework Upgrades Confident Asset Management Limited tech_lead Dalya Blecher dalya name
Credeq - Azure Migration Credeq account_manager Marsh Middleton marsh.middleton name
Credeq - Azure Migration Credeq project_manager Louis van der Walt louisvdw name
Credeq - Azure Migration Credeq tech_lead Rui Felix rui name
Credeq - Guarantee Gateway - Phase 1 Dev Credeq account_manager Marsh Middleton marsh.middleton name
Credeq - Guarantee Gateway - Phase 1 Dev Credeq project_manager Jessica-May Bergh jessica-may name
Credeq - Guarantee Gateway - Phase 1 Dev Credeq tech_lead Luke Holmwood lukeh name
Credeq Cloudflare Implementation Credeq account_manager Nikita Venter nikita name
Credeq Cloudflare Implementation Credeq project_manager Louis van der Walt louisvdw name
Credeq Cloudflare Implementation Credeq tech_lead Jared Naude jared name
Credeq Google Cloud Strategic Agreement Credeq account_manager Nikita Venter nikita name
Credeq Google Cloud Strategic Agreement Credeq project_manager Louis van der Walt louisvdw name
Credeq Google Cloud Strategic Agreement Credeq tech_lead Louis-Philip Shahim louis-philip name
Rapid Data Delivery POC Discovery Insure account_manager Nikita Venter nikita name
Rapid Data Delivery POC Discovery Insure tech_lead Nick Walker nick name
Investec IFB SOW 1 & SOW 2 Investec Bank account_manager Devina Naidoo devina name
Investec IFB SOW 1 & SOW 2 Investec Bank project_manager Nitesh Dhoogar nitesh name
Investec IFB SOW 1 & SOW 2 Investec Bank tech_lead Jordan Smith jordan name
Cloudflare Engineering Lombard account_manager Nikita Venter nikita name
Cloudflare Engineering Lombard project_manager Craig Fuchs craigf name
Cloudflare Engineering Lombard tech_lead Jared Naude jared name
Lombard - CloudM Migration Lombard account_manager Marsh Middleton marsh.middleton name
Lombard - CloudM Migration Lombard project_manager Chanel Barnard chanel.barnard name
Lombard - CloudM Migration Lombard tech_lead Francois Botha francois name
Lombard GCP Capacity Upsell on Strat Contract Lombard account_manager Nikita Venter nikita name
Lombard GCP Capacity Upsell on Strat Contract Lombard project_manager Craig Fuchs craigf name
Lombard GCP Capacity Upsell on Strat Contract Lombard tech_lead Muhammad Muneeb muhammad.muneeb name
Lombard Github Migration Lombard account_manager Nikita Venter nikita name
Lombard Github Migration Lombard project_manager Craig Fuchs craigf name
Lombard Github Migration Lombard tech_lead Mark McNaughton mark name
Lombard Google Cloud Strategic Agreement Lombard account_manager Nikita Venter nikita name
Lombard Google Cloud Strategic Agreement Lombard project_manager Craig Fuchs craigf name
Lombard Google Cloud Strategic Agreement Lombard tech_lead Louis-Philip Shahim louis-philip name
OM Bank Control Tower Old Mutual Bank account_manager Yolande Roberts yolande name
OM Bank Control Tower Old Mutual Bank project_manager Kovishnee Moodley kovishnee name
OM Bank Control Tower Old Mutual Bank tech_lead Naseem Ahmed naseem name
Osiris Business Analyst Osiris account_manager Ajendra Jaggeth ajendra name
Osiris Business Analyst Osiris project_manager Craig Fuchs craigf name
Osiris Business Analyst Osiris tech_lead Dipti Kabra dipti.kabra name
Multi Contract Consolidation & Extension Osiris Trading account_manager Ajendra Jaggeth ajendra name
Multi Contract Consolidation & Extension Osiris Trading project_manager Craig Fuchs craigf name
Multi Contract Consolidation & Extension Osiris Trading tech_lead Kudzai Muranga kudzai name
Osiris - Scytale - ITGC Automation Software Osiris Trading account_manager Jonathan Matus JM01 name
PayInc Strat Bucket PayInc account_manager Devina Naidoo devina name
PayInc Strat Bucket PayInc tech_lead Naseem Ahmed naseem name
ShareForce_Build Project ShareForce account_manager Devina Naidoo devina name
ShareForce_Build Project ShareForce project_manager Jessica-May Bergh jessica-may name
ShareForce_Build Project ShareForce tech_lead Michaela Kruger michaela alias
SBG | Challenger Squad | SOW 1 Standard Bank account_manager Lesley van den Heever lesley name
SBG | Challenger Squad | SOW 1 Standard Bank project_manager Gisela Tiedt — unmatched
SBG | Challenger Squad | SOW 1 Standard Bank tech_lead Matthew Crockett matthew name
SBSA - Enterprise AI platform - Datahandling -Sage Maker Studio Standard Bank account_manager Lesley van den Heever lesley name
SBSA - Enterprise AI platform - Datahandling -Sage Maker Studio Standard Bank tech_lead Ryan Harris ryan name
SBSA - SmartVista Extension and Architecture Review Standard Bank account_manager Lesley van den Heever lesley name
SBSA - SmartVista Extension and Architecture Review Standard Bank tech_lead Clifford Levi clifford name
SBSA Business Online+ Extension Standard Bank account_manager Lesley van den Heever lesley name
SBSA Business Online+ Extension Standard Bank tech_lead Taona Madawo taona name
SBSA | Automation Framework | AI Credit Analyst | CrediAssist POC Standard Bank account_manager Lesley van den Heever lesley name
SBSA | Automation Framework | AI Credit Analyst | CrediAssist POC Standard Bank project_manager Jyothi Reddy jyothi.reddy name
SBSA | Automation Framework | AI Credit Analyst | CrediAssist POC Standard Bank tech_lead Jonathan Sidney jonathan name
SBSA-AWS Pro-Serve GenAI Enterprise Platform MVP 1 Standard Bank account_manager Lesley van den Heever lesley name
SBSA-AWS Pro-Serve GenAI Enterprise Platform MVP 1 Standard Bank tech_lead Miguel Laranjeira miguel name
Strate | Team Aug | Confluent Engineering Consulting | Project EXT (2months) Strate account_manager Lesley van den Heever lesley name
Strate | Team Aug | Confluent Engineering Consulting | Project EXT (2months) Strate tech_lead Bhavesh Sooka bhavesh name
Swiffy | New Landing Zone | Old Enviro Migration | Cloud PS Swiffy account_manager Lesley van den Heever lesley name
Swiffy | New Landing Zone | Old Enviro Migration | Cloud PS Swiffy project_manager Kovishnee Moodley kovishnee name
Swiffy | New Landing Zone | Old Enviro Migration | Cloud PS Swiffy tech_lead Naseem Ahmed naseem name
Principal Solution Architect (1 August 2026 - 30 June 2027)) University of Cambridge account_manager Darren Bak darren name
Principal Solution Architect (1 August 2026 - 30 June 2027)) University of Cambridge tech_lead Anthony Patton anthony.patton name
Principal Solution Architect (11 Oct 2025 - 31 Jul 2026) University of Cambridge account_manager Darren Bak darren name
Principal Solution Architect (11 Oct 2025 - 31 Jul 2026) University of Cambridge tech_lead Anthony Patton anthony.patton name
University of Cambridge - API Platform team extension University of Cambridge account_manager Darren Bak darren name
University of Cambridge - API Platform team extension University of Cambridge tech_lead Jonathan Lew jonathanl name
University of Cambridge - B2B Shop lead University of Cambridge account_manager Darren Bak darren name
University of Cambridge - B2B Shop lead University of Cambridge tech_lead Preshalin Naidoo preshalin name
University of Cambridge - Design system lead University of Cambridge account_manager Darren Bak darren name
University of Cambridge - Design system lead University of Cambridge tech_lead Henko Germishuizen henko.germishuizen name
Engen Website Maintenance and Support - Extension Vivo Energy PLC account_manager Mia Van Sittert mia name
Engen Website Maintenance and Support - Extension Vivo Energy PLC project_manager Craig Fuchs craigf name
Engen Website Maintenance and Support - Extension Vivo Energy PLC tech_lead Vin Mushwana vin.mushwana name
VEOne Program Squad - Extension Vivo Energy PLC account_manager Mia Van Sittert mia name
VEOne Program Squad - Extension Vivo Energy PLC project_manager Barkley van Wyngaard barkley.vanwyngaard name
VEOne Program Squad - Extension Vivo Energy PLC tech_lead Jannes Kruger jannes.kruger name
WFS Control Tower Upgrade Woolworths Financial Services account_manager Mia Van Sittert mia name
WFS Control Tower Upgrade Woolworths Financial Services project_manager Kovishnee Moodley kovishnee name
WFS Control Tower Upgrade Woolworths Financial Services tech_lead Naseem Ahmed naseem name

Detailed project commentary

Drill-through from tracker projects into raw detailed_projects.csv commentary. Responses marked out of score are still shown because they often explain the rating.

Absa - Digital Transformation Migration 80 rows
Section Question Response Score Notes
Context Client / Customer Account Capture the client or account name for account-level risk reporting. — —
Context Primary Focus for This Period Target 36 Applications to migration — —
Context Key Wins / Achievements None to date — —
Context Biggest Challenges / Blockers Applications not being migration ready — —
Context Major Changes (scope/team/budget/customer) None — —
Context Funding / Commercial Changes None — —
Context Single Most Important Leadership / Account Escalation — — —
Budget Health Budget type T&M — T&M
Budget Health Total budget vs utilised budget — 5 —
Budget Health % of timeline elapsed vs % of budget consumed — 5 —
Budget Health Is spend aligned with progress? — 5 —
Budget Health Forecasted overrun or underrun risk — 5 —
Budget Health Cost inefficiencies or rework impacting budget — 5 —
Budget Health Confidence in budget landing — 5 —
Budget Health Budget Health Section Score — 5 Auto-calculated
Timeline Health Key milestones on track — — —
Timeline Health % of planned work completed vs expected — 3 Targeted applications are behind delivery due to complexity of applications in this current wave
Timeline Health Schedule slippage this period — 3 Targeted applications are behind delivery due to complexity of applications in this current wave
Timeline Health Dependencies impacting timelines — 3 Targeted applications are behind delivery due to complexity of applications in this current wave
Timeline Health Confidence in delivery dates — 3 Targeted applications are behind delivery due to complexity of applications in this current wave
Timeline Health Timeline Health Section Score — 3 Auto-calculated
Delivery Health Consistency of delivery this period — 4 Synthesis team are delivering, however overall programme is under strain due to complexity of applications in this phase
Delivery Health Velocity trend — 3 Delivery of apps is not at speed as per past waves due to complexity of apps
Delivery Health Customer value delivered — 4 Customer value is impacted by delivery of apps beyond our control
Delivery Health Flow bottlenecks or blockers — 3 —
Delivery Health % of committed vs delivered work — 3 No apps as yet delivered in Q2, due to app complexity and changes
Delivery Health Delivery Health Section Score — 3.40 Auto-calculated
Scope Health Scope clarity — — —
Scope Health Scope stability — — —
Scope Health Change control effectiveness — — —
Scope Health Impact of scope changes on budget/timeline — — —
Scope Health Backlog health — — —
Scope Health Scope Health Section Score — — Auto-calculated
Quality Engineering quality — — —
Quality Compliance / data privacy / security — — —
Quality Defect and rework trend — — —
Quality Test coverage and automation maturity — — —
Quality Production incidents or escaped defects — — —
Quality Data classification and handling — — —
Quality Access controls and environment segregation — — —
Quality Data storage and protection controls — — —
Quality AI data usage controls — — —
Quality Breaches, near misses, or governance gaps — — —
Quality Compliance confidence — — —
Quality Quality Section Score — — Auto-calculated
Team Wellbeing Team morale — 3 Delay in application delivery does have a halo effect on the team, although not our direct involvement
Team Wellbeing Workload sustainability — 5 Workload is sustainable
Team Wellbeing Burnout risk — 5 No risk to burnout, work capacity is significantly less than previous waves
Team Wellbeing Team stability — 5 Team are comfortable
Team Wellbeing Psychological safety and collaboration — 5 Team are well supported
Team Wellbeing Team Wellbeing Section Score — 4.60 Auto-calculated
Customer Satisfaction & Engagement Customer sentiment — 5 Customer is positive and supportive of new roles within Synthesis team
Customer Satisfaction & Engagement Customer engagement level — 5 Customer is fully engaged and proactive
Customer Satisfaction & Engagement Feedback received this period — 3 Informal feedback received during this period but no formal feedback
Customer Satisfaction & Engagement Responsiveness and collaboration — 5 Customer is fully responsive to all questions and queries
Customer Satisfaction & Engagement Escalations or relationship strain — 5 No esclations relating to Syntheis
Customer Satisfaction & Engagement Customer Satisfaction & Engagement Section Score — 4.60 Auto-calculated
Risk & Issue Management Active risks visibility — 3 App delivery due to complexity of applications and changes to app delivery requirements
Risk & Issue Management Active issues visibility — 3 —
Risk & Issue Management Mitigation effectiveness — 3 Steps in place with Absa team to mitigate
Risk & Issue Management Critical risks or issues — 3 —
Risk & Issue Management Issue resolution responsiveness — 3 —
Risk & Issue Management Emerging risks / early warning signals — 3 —
Risk & Issue Management Risk & Issue Management Section Score — 3 Auto-calculated
Funding Milestone Targets Funding milestones in scope? No — If applicable, describe milestones and current position.
Funding Milestone Targets Are milestone targets being met? — — —
Funding Milestone Targets Funding Milestone Targets Section Score / Rule Check — — If D82 = Yes and E83 <= 3, the Budget Health shown in the summary and overall weighted score is capped at 3 (Amber max).
AI Usage & Maturity Where is AI being used? — — —
AI Usage & Maturity % of team actively using AI — — —
AI Usage & Maturity AI maturity level — — —
AI Usage & Maturity Observed impact from AI — — —
AI Usage & Maturity AI Usage & Maturity Section Score — — Auto-calculated
Tech Stack Used Current tech stack — — —
Tech Stack Used Material tech changes this period — — —
Tech Stack Used Tech risks or constraints — — —
Tech Stack Used Tech Stack Used Section Score — — Auto-calculated
New Opportunities Upsell / cross-sell opportunities — — —
New Opportunities Efficiency / delivery improvement opportunities — — —
New Opportunities Innovation or expansion opportunities — — —
New Opportunities New Opportunities Section Score — — Auto-calculated
Absa - Loan Optimisation 80 rows
Section Question Response Score Notes
Context Client / Customer Account Absa Bank — —
Context Primary Focus for This Period Pilot period enhancements and improvements based on user feedback. Governance activities for go live. — —
Context Key Wins / Achievements Implemented a checklist feature to pave the way for future Salesforce integration, implemented a dashboard to support KPI reporting — —
Context Biggest Challenges / Blockers not a blocker, however governance process unknown and untested (especially in terms of AI) — —
Context Major Changes (scope/team/budget/customer) None — —
Context Funding / Commercial Changes None — —
Context Single Most Important Leadership / Account Escalation N/A — —
Budget Health Budget type T&M — —
Budget Health Total budget vs utilised budget Over budget by R14152,83 or 0.001% 5 —
Budget Health % of timeline elapsed vs % of budget consumed — 5 —
Budget Health Is spend aligned with progress? On Track 5 —
Budget Health Forecasted overrun or underrun risk — 5 —
Budget Health Cost inefficiencies or rework impacting budget — 5 —
Budget Health Confidence in budget landing High 5 —
Budget Health Budget Health Section Score — 5 Auto-calculated
Timeline Health Key milestones on track on track 5 —
Timeline Health % of planned work completed vs expected as expected 5 —
Timeline Health Schedule slippage this period None 5 —
Timeline Health Dependencies impacting timelines None 5 —
Timeline Health Confidence in delivery dates High 5 —
Timeline Health Timeline Health Section Score — 5 Auto-calculated
Delivery Health Consistency of delivery this period — 5 —
Delivery Health Velocity trend Stable 5 —
Delivery Health Customer value delivered — 5 —
Delivery Health Flow bottlenecks or blockers Unclear governance path to prod 4 —
Delivery Health % of committed vs delivered work — 5 —
Delivery Health Delivery Health Section Score — 4.80 Auto-calculated
Scope Health Scope clarity Yes 5 —
Scope Health Scope stability Managing creep in sprints 4 —
Scope Health Change control effectiveness — 5 —
Scope Health Impact of scope changes on budget/timeline — 5 —
Scope Health Backlog health Refined 5 —
Scope Health Scope Health Section Score — 4.80 Auto-calculated
Quality Engineering quality — 5 —
Quality Compliance / data privacy / security PI Data on Absa network only 5 —
Quality Defect and rework trend — 5 —
Quality Test coverage and automation maturity — 5 —
Quality Production incidents or escaped defects — 5 —
Quality Data classification and handling PI Data on Absa network only 5 —
Quality Access controls and environment segregation Access controls and segreagation in place 5 —
Quality Data storage and protection controls — 5 —
Quality AI data usage controls Fully aligned to Absa policy 5 —
Quality Breaches, near misses, or governance gaps none 5 —
Quality Compliance confidence High 5 —
Quality Quality Section Score — 5 Auto-calculated
Team Wellbeing Team morale High 5 —
Team Wellbeing Workload sustainability Sustainable 5 —
Team Wellbeing Burnout risk none 5 —
Team Wellbeing Team stability — 5 —
Team Wellbeing Psychological safety and collaboration — 5 —
Team Wellbeing Team Wellbeing Section Score — 5 Auto-calculated
Customer Satisfaction & Engagement Customer sentiment Positive 5 —
Customer Satisfaction & Engagement Customer engagement level Active 4 —
Customer Satisfaction & Engagement Feedback received this period — 5 —
Customer Satisfaction & Engagement Responsiveness and collaboration — 5 —
Customer Satisfaction & Engagement Escalations or relationship strain None 5 —
Customer Satisfaction & Engagement Customer Satisfaction & Engagement Section Score — 4.80 Auto-calculated
Risk & Issue Management Active risks visibility Yes 5 —
Risk & Issue Management Active issues visibility Yes 5 —
Risk & Issue Management Mitigation effectiveness Mitigation processes not always clear 4 —
Risk & Issue Management Critical risks or issues — 5 —
Risk & Issue Management Issue resolution responsiveness — 4 —
Risk & Issue Management Emerging risks / early warning signals — 4 —
Risk & Issue Management Risk & Issue Management Section Score — 4.50 Auto-calculated
Funding Milestone Targets Funding milestones in scope? Yes 5 If applicable, describe milestones and current position.
Funding Milestone Targets Are milestone targets being met? — 5 —
Funding Milestone Targets Funding Milestone Targets Section Score / Rule Check — 5 If D82 = Yes and E83 <= 3, the Budget Health shown in the summary and overall weighted score is capped at 3 (Amber max).
AI Usage & Maturity Where is AI being used? Development, Delivery, Testing — —
AI Usage & Maturity % of team actively using AI likely around 60% — —
AI Usage & Maturity AI maturity level Embedded trending to optimised — —
AI Usage & Maturity Observed impact from AI Proof of value is clear, speed, productivity and customer value particularly — —
AI Usage & Maturity AI Usage & Maturity Section Score — — Auto-calculated
Tech Stack Used Current tech stack AWS, Databricks, ADO, Claude LLM — —
Tech Stack Used Material tech changes this period Nothing at this stage — —
Tech Stack Used Tech risks or constraints None at this stage — —
Tech Stack Used Tech Stack Used Section Score — — Auto-calculated
New Opportunities Upsell / cross-sell opportunities Within CIB digital — —
New Opportunities Efficiency / delivery improvement opportunities — — —
New Opportunities Innovation or expansion opportunities — — —
New Opportunities New Opportunities Section Score — — Auto-calculated
API PLatform Team 178 rows
Section Question Response Score Notes
Context Client / Customer Account Cambridge University — —
Context Primary Focus for This Period Auditing & logging token operations Enable storage of Client details (in Kafka) Configure CICD to dev-1 & test-1 Client credentials api endpoints Api request logging — —
Context Key Wins / Achievements Completed the first API and deployed to testing for Galar team to start testing — —
Context Biggest Challenges / Blockers The time Specs take to get approved and delivered to us — —
Context Major Changes (scope/team/budget/customer) Accounting for AI in sizing, Darren has requested that they dont add that into sizing. — —
Context Funding / Commercial Changes — — —
Context Single Most Important Leadership / Account Escalation Need to get approval on extension and or team merges — —
Budget Health Budget type — — —
Budget Health Total budget vs utilised budget Slight overburn for the month, but all stil under control. Overall the project is showing a slight under burn 4 —
Budget Health % of timeline elapsed vs % of budget consumed Slight overburn for the month, but all stil under control. Overall the project is showing a slight under burn 4 —
Budget Health Is spend aligned with progress? Yes 4 —
Budget Health Forecasted overrun or underrun risk Underrun but not concern 4 —
Budget Health Cost inefficiencies or rework impacting budget No 5 —
Budget Health Confidence in budget landing Little underburn but managble 4 —
Budget Health Budget Health Section Score — 4.25 Auto-calculated
Timeline Health Key milestones on track We have a clear plan and roadmap and we are currently slightly behind but catching up fast. As it stand now I think we will make our planned deadline 3 —
Timeline Health % of planned work completed vs expected I think we have less than 5% of the required work completed but it should move faster from here 2 —
Timeline Health Schedule slippage this period There was a long delay for us to get the approved specs for the first few features, but now that we have it we are getting some traction 2 —
Timeline Health Dependencies impacting timelines Key man dependency on Toby to provide us with approved specs but this has imoroved and we have received specs for the next bundle of work already. So this is imprving a lot 3 —
Timeline Health Confidence in delivery dates even though we had a slow start I do feel we are picking up speed and should start delivering better from here on in 3 —
Timeline Health Timeline Health Section Score — 2.50 Auto-calculated
Delivery Health Consistency of delivery this period Even though we had a slow start, once we started delivering we have kept up the momentum 4 —
Delivery Health Velocity trend Oor value flow (value delivered for the customer) We did not know our velocity previously but since we started tracking it it seems to be increasing sprint to sprint. This is also indication that we are getting some traction. 4 —
Delivery Health Customer value delivered Even though we have not delivered much we are starting to deliver value for the Galar team to start consuming. 4 —
Delivery Health Flow bottlenecks or blockers Key man dependency on Toby to provide us with approved specs but this has imoroved and we have received specs for the next bundle of work already. So this is imprving a lot 3 —
Delivery Health % of committed vs delivered work So far for the sprint we planned we have delivered well against what was planned 4 —
Delivery Health Delivery Health Section Score — 3.80 Auto-calculated
Scope Health Scope clarity It is clear what we need to develop 4 —
Scope Health Scope stability Some portion of the scope especially scope for later in the year is a little bit up in the air but continuously being discussed 3 —
Scope Health Change control effectiveness We have not experienced any change in scope over the last month 4 —
Scope Health Impact of scope changes on budget/timeline N/A 4 —
Scope Health Backlog health There is more than enough work to keep us busy until next year 5 —
Scope Health Scope Health Section Score — — Auto-calculated
Quality Engineering quality We have not deployed to prod as yet and only testing in Dev. But so far the quality seems to be good. We will get a better picture of quality once QA has been sorted. 4 —
Quality Compliance / data privacy / security This is managed by Cambridge and they are quite data sensitive with very strict security measures 4 Add details here
Quality Defect and rework trend We have not deployed to prod as yet and only testing in Dev. But so far the quality seems to be good. We will get a better picture of quality once QA has been sorted. 4 —
Quality Test coverage and automation maturity So for for where we are in the project I think this is good 4 —
Quality Production incidents or escaped defects N/A — —
Quality Data classification and handling This is managed by Cambridge and they are quite data sensitive with very strict security measures 5 Add details here
Quality Access controls and environment segregation This is managed by Cambridge and they are quite data sensitive with very strict security measures 5 —
Quality Data storage and protection controls This is managed by Cambridge and they are quite data sensitive with very strict security measures 5 —
Quality AI data usage controls We are following Synthesis standards 5 —
Quality Breaches, near misses, or governance gaps N/A — —
Quality Compliance confidence — 5 —
Quality Quality Section Score — 4.56 Auto-calculated
Team Wellbeing Team morale Team morale has picked up a lot and I think it is in a good state 4 —
Team Wellbeing Workload sustainability We went from working but not being clear on what to do, to having a clear goal so I think this is under control 5 —
Team Wellbeing Burnout risk As it stands now I think burnout risk is low 5 —
Team Wellbeing Team stability Team stability seems good and I dont pick up any warning signs as yet 5 —
Team Wellbeing Psychological safety and collaboration — 5 —
Team Wellbeing Team Wellbeing Section Score — 4.80 Auto-calculated
Customer Satisfaction & Engagement Customer sentiment Seeing we are one piece in a larger puzzle this is a difficult question to ask. I think the client is nervous but more so because they struggled to get some traction and working with multiple teams and all affect each other. So for now I would say the client is nervous but more so because it took a long time for us to get going. So not so much negative towards us specifically but more so to the project. We have however provided the client with a clear roudmap which has settled the nerves a lot 4 —
Customer Satisfaction & Engagement Customer engagement level The customer is very engaged 5 —
Customer Satisfaction & Engagement Feedback received this period No direct negative or positive feedback received as yet — —
Customer Satisfaction & Engagement Responsiveness and collaboration Collaboration between us and client is good and they are responsive even if it sometimes takes long to get things from them 4 —
Customer Satisfaction & Engagement Escalations or relationship strain I think this is getting better 3 —
Customer Satisfaction & Engagement Customer Satisfaction & Engagement Section Score — 4 Auto-calculated
Risk & Issue Management Active risks visibility The customer is responsible for managing the projects and we have been servicing risks. I do think we can do better in documenting and recording risks 4 —
Risk & Issue Management Active issues visibility The customer is responsible for managing the projects and we have been servicing risks. I do think we can do better in documenting and recording risks 3 —
Risk & Issue Management Mitigation effectiveness The customer does try and mitigate risk and so far have been effective in mitigating risks raised 4 —
Risk & Issue Management Critical risks or issues QA is a risk and also the speed of spec being done and approved. The uncertainty around the payment features is also a risk and might cause delays once we know complexity or system to use 2 —
Risk & Issue Management Issue resolution responsiveness When issues are experienced the client is quick to action but resolutions do take long because of red tape 2 —
Risk & Issue Management Emerging risks / early warning signals Timeline pressure is still a risk and we need to define the payment features and integrations as soon as possible 3 —
Risk & Issue Management Risk & Issue Management Section Score — 3 Auto-calculated
Funding Milestone Targets Funding milestones in scope? Yes — If applicable, describe milestones and current position.
Funding Milestone Targets Are milestone targets being met? — 4 —
Funding Milestone Targets Funding Milestone Targets Section Score / Rule Check — 4 If D82 = Yes and E83 <= 3, the Budget Health shown in the summary and overall weighted score is capped at 3 (Amber max).
AI Usage & Maturity Confirm with the customer if the use of AI is permitted for the project Yes we are working with the client in implementing AI assisted processes in both planning and development — —
AI Usage & Maturity Where is AI being used? Specs and development — —
AI Usage & Maturity % of team actively using AI 1 — —
AI Usage & Maturity AI maturity level Embedded — —
AI Usage & Maturity Observed impact from AI So far we could see increase in speed but becuase we did not track previous sprint velocities we dont have anything to compare it to — —
AI Usage & Maturity How are the team using AI on the project? Requirement generation and coding — —
AI Usage & Maturity AI Usage & Maturity Section Score — — Auto-calculated
Tech Stack Used Current tech stack — — —
Tech Stack Used Material tech changes this period — — —
Tech Stack Used Tech risks or constraints — — —
Tech Stack Used Tech Stack Used Section Score — — Auto-calculated
Project Compliance Does the project handle senstive data? Not as yet — —
Project Compliance What regulations must be complied with GDPR — —

Showing first 80 of 178 commentary rows for this project.

April 2026_FraudMLEngineering_Comprehensive Internal Project Health Report 80 rows
Section Question Response Score Notes
Context Client / Customer Account Capitec Bank — —
Context Primary Focus for This Period Investigate ensemble — —
Context Key Wins / Achievements External transfer model working and doing well — —
Context Biggest Challenges / Blockers Problem is legitimately difficult — —
Context Major Changes (scope/team/budget/customer) Not a standard clearly solvable problem — —
Context Funding / Commercial Changes — — —
Context Single Most Important Leadership / Account Escalation N/A — —
Budget Health Budget type — — —
Budget Health Total budget vs utilised budget — 5 —
Budget Health % of timeline elapsed vs % of budget consumed — 5 —
Budget Health Is spend aligned with progress? — 5 —
Budget Health Forecasted overrun or underrun risk — 5 —
Budget Health Cost inefficiencies or rework impacting budget — 5 —
Budget Health Confidence in budget landing — 5 —
Budget Health Budget Health Section Score — 5 Auto-calculated
Timeline Health Key milestones on track — 4 —
Timeline Health % of planned work completed vs expected — 4 —
Timeline Health Schedule slippage this period — 4 —
Timeline Health Dependencies impacting timelines — 4 —
Timeline Health Confidence in delivery dates — 4 —
Timeline Health Timeline Health Section Score — 4 Auto-calculated
Delivery Health Consistency of delivery this period — 5 —
Delivery Health Velocity trend — 4 —
Delivery Health Customer value delivered — 5 —
Delivery Health Flow bottlenecks or blockers — 4 —
Delivery Health % of committed vs delivered work — 4 —
Delivery Health Delivery Health Section Score — 4.40 Auto-calculated
Scope Health Scope clarity — 5 —
Scope Health Scope stability — 5 —
Scope Health Change control effectiveness — 5 —
Scope Health Impact of scope changes on budget/timeline — 4 —
Scope Health Backlog health — 4 —
Scope Health Scope Health Section Score — — Auto-calculated
Quality Engineering quality — 5 —
Quality Compliance / data privacy / security — 5 —
Quality Defect and rework trend — 4 —
Quality Test coverage and automation maturity — 3 —
Quality Production incidents or escaped defects — 4 —
Quality Data classification and handling — 5 —
Quality Access controls and environment segregation — 5 —
Quality Data storage and protection controls — 5 —
Quality AI data usage controls — 5 —
Quality Breaches, near misses, or governance gaps — 5 —
Quality Compliance confidence — 5 —
Quality Quality Section Score — 4.64 Auto-calculated
Team Wellbeing Team morale — 4 —
Team Wellbeing Workload sustainability — 5 —
Team Wellbeing Burnout risk — 4 —
Team Wellbeing Team stability — 5 —
Team Wellbeing Psychological safety and collaboration — 5 —
Team Wellbeing Team Wellbeing Section Score — 4.60 Auto-calculated
Customer Satisfaction & Engagement Customer sentiment — 4 —
Customer Satisfaction & Engagement Customer engagement level — 5 —
Customer Satisfaction & Engagement Feedback received this period — 4 —
Customer Satisfaction & Engagement Responsiveness and collaboration — 3 —
Customer Satisfaction & Engagement Escalations or relationship strain — 4 —
Customer Satisfaction & Engagement Customer Satisfaction & Engagement Section Score — 4 Auto-calculated
Risk & Issue Management Active risks visibility — 5 —
Risk & Issue Management Active issues visibility — 5 —
Risk & Issue Management Mitigation effectiveness — 5 —
Risk & Issue Management Critical risks or issues — 5 —
Risk & Issue Management Issue resolution responsiveness — 4 —
Risk & Issue Management Emerging risks / early warning signals — 5 —
Risk & Issue Management Risk & Issue Management Section Score — 4.83 Auto-calculated
Funding Milestone Targets Funding milestones in scope? No — If applicable, describe milestones and current position.
Funding Milestone Targets Are milestone targets being met? — — —
Funding Milestone Targets Funding Milestone Targets Section Score / Rule Check — — If D82 = Yes and E83 <= 3, the Budget Health shown in the summary and overall weighted score is capped at 3 (Amber max).
AI Usage & Maturity Where is AI being used? — 5 —
AI Usage & Maturity % of team actively using AI — 5 —
AI Usage & Maturity AI maturity level — 5 —
AI Usage & Maturity Observed impact from AI — 3 —
AI Usage & Maturity AI Usage & Maturity Section Score — — Auto-calculated
Tech Stack Used Current tech stack — 5 Sagemaker, Python, XGboost
Tech Stack Used Material tech changes this period — 5 None
Tech Stack Used Tech risks or constraints — 5 None
Tech Stack Used Tech Stack Used Section Score — — Auto-calculated
New Opportunities Upsell / cross-sell opportunities — — Unclear
New Opportunities Efficiency / delivery improvement opportunities — — Unclear
New Opportunities Innovation or expansion opportunities — — Unlikely
New Opportunities New Opportunities Section Score — — Auto-calculated
April 2026_Team Augmentation_Change Request AV003_Comprehensive Internal Project Health Report 80 rows
Section Question Response Score Notes
Context Client / Customer Account Capture the client or account name for account-level risk reporting. — —
Context Primary Focus for This Period Complete if there was a goal for this month — —
Context Key Wins / Achievements -Zander is getting exposure to new domains (data governance, Snowflake, agentic solutions) - Customer is incredibly understanding and he gets to work directly with the CEO. Learning to manage customer expectations and prioritise with them. It is clear that he is thinking logically through all of these delivery practices. - Zander is comfortable with the accountability he has had to take on and is comfortable working in an augmentation engagement. — —
Context Biggest Challenges / Blockers — — —
Context Major Changes (scope/team/budget/customer) — — —
Context Funding / Commercial Changes — — —
Context Single Most Important Leadership / Account Escalation — — —
Budget Health Budget type Time & Materials Engagement — —
Budget Health Total budget vs utilised budget Tayla to confirm % variance 4 March had a slight under budget amount noted which decreased starting utilisation
Budget Health % of timeline elapsed vs % of budget consumed — 4 —
Budget Health Is spend aligned with progress? — 4 —
Budget Health Forecasted overrun or underrun risk — 5 Budget may be recovered based on actual number of days per month versus the 21 days commercials are typcially based on. Public holidays may impact further and should be monitored.
Budget Health Cost inefficiencies or rework impacting budget None to be reported. 5 —
Budget Health Confidence in budget landing Medium 5 Uncertainty regarding leave, actual working days etc. may result in minor under utilisation (accepted risk)
Budget Health Budget Health Section Score — 4.50 Auto-calculated
Timeline Health Key milestones on track Timelines will need to be adjutsed on their current plan 4 —
Timeline Health % of planned work completed vs expected There’s quite a bit of work planned, especially around the grant-related tasks. I’ve already put together the documentation, timelines, resourcing, budgeting, and overall project plans. At the moment, we’re just waiting for the grant approval before we can move forward with those, which may take a few months. On the unplanned side, things are progressing well. The liquidity model is currently in the testing phase, although there are still some dependencies on internal stakeholders for data. The risk model will need to be refactored, but for now, the liquidity model is the main priority. Data governance is also coming along nicely and making good progress. 4 —
Timeline Health Schedule slippage this period The majority of the work I’m currently handling is structured as rolling milestones, primarily due to the size and complexity of the tasks. Much of the work is delivered as vertical slices, meaning each piece needs to be fully completed before it can be properly tested and validated. Where there have been delays, they have generally been driven by external dependencies or shifts in direction from upper management. In some cases, this has introduced additional scope, which has impacted timelines. 3.50 —
Timeline Health Dependencies impacting timelines Internal 4 —
Timeline Health Confidence in delivery dates Medium. I have some concerns around timeline delivery for both the data governance and grant-related tasks. These are fairly large pieces of work and require significant cross-collaboration with multiple stakeholders. Factors such as Jewish public holidays and South African public holidays can also impact availability and progress. In addition, the grant tasks are quite complex, which increases the likelihood of timelines extending beyond initial estimates. That said, I’ve already discussed this with the team lead and CEO, and we’re aligned that if timelines do need to be extended, it won’t be an issue. 3 —
Timeline Health Timeline Health Section Score — 3.50 Auto-calculated
Delivery Health Consistency of delivery this period In my check-ins with the team lead and Joni, I’ve consistently received positive feedback on my velocity and delivery. 4 —
Delivery Health Velocity trend My velocity is generally stable, and I’d consider it high. However, dependencies on others can sometimes impact delivery timelines. This is well understood by both my team lead and the CEO. The positive side is that the foundational work I’m doing now will improve my velocity over time, especially once we move into a more in-depth implementation phase. 4 —
Delivery Health Customer value delivered Yes, some of my work has already been used to help assess the current state of annual financial planning, as well as the underlying statistics and assumptions. The risk model is still a bit unstable at this stage due to a misalignment around the fields used, but that’s being addressed. I’m also confident that the data governance framework I’m working on will bring significant benefits once implemented. 4 —
Delivery Health Flow bottlenecks or blockers There are a few bottlenecks on the project, mostly related to dependencies on others to provide or capture the data I need to move forward. These are being actively addressed in stand-ups and weekly sessions. If a blocker takes too long, I usually find a workaround to keep things moving. That said, data hasn’t always been treated as a top priority, but this is gradually improving, which is a positive shift for the project. 3 —
Delivery Health % of committed vs delivered work It’s difficult to assign an exact percentage of work completed, as there are multiple streams running in parallel, which isn’t always ideal. However, everything I’ve worked on is actively progressing and being brought to completion. Priorities can sometimes delay immediate progress on certain tasks, but they do get completed over time. Overall, I’d estimate around 80% completion with 100% commitment. 4 —
Delivery Health Delivery Health Section Score — 3.80 Auto-calculated
Scope Health Scope clarity Yes, there was some initial uncertainty around the scope at the start of the project. However, I aligned with the team lead, and we now have bi-weekly check-ins to ensure everything stays on track. I also have weekly sessions with the CEO to keep everyone aligned and on the same page. 5 —
Scope Health Scope stability Most of my work at Avenews is grant-related, but there has been some scope creep with additional responsibilities like data governance implementation, the risk model, and the liquidity model. This has been manageable so far since I haven’t fully started on the grant tasks yet. However, it could become a challenge once I’m actively working on grant deliverables alongside these additional responsibilities. 3 —
Scope Health Change control effectiveness Yes, when changes are needed to a model or query, we usually align on a common approach and way of working. If there’s a difference in opinion, I make my perspective clear and explain the reasoning behind it. 4 —
Scope Health Impact of scope changes on budget/timeline This has been an ongoing challenge at Avenews. I’ve discussed it with my team lead and have made it a habit to raise any timeline-related risks as early as possible. The positive side is that both the team lead and CEO understand that a startup environment is highly dynamic, so some level of uncertainty comes with it. Another factor is that grant-related tasks don’t always have clearly defined timelines, which can impact how much work can be completed before timelines are finalised. 3 —
Scope Health Backlog health Avenews does run sprints, but the process isn’t always consistently managed, and transitions between sprints can sometimes feel unstructured. The team lead is aware of this and is actively working on standardising the process. From my side, I have full visibility of my workload and keep the board clean and up to date. As the only ML Engineer on the team, I’m responsible for creating and managing my own tickets. 4 —
Scope Health Scope Health Section Score — — Auto-calculated
Quality Engineering quality There are test scripts in the repository for the endpoints, along with API documentation, and Avenews also has a dedicated tester. Since I mainly work on the back-end, I make sure tests are in place and validate queries in the CRM together with the subject matter expert. 4 —
Quality Compliance / data privacy / security As mentioned previously, this is still a work in progress and will be addressed as part of the data governance implementation. So far, everything has been running smoothly, and as the business continues to mature quickly, we’ll be introducing stronger security and compliance measures. 3 —
Quality Defect and rework trend Reworking the model logic itself hasn’t really been an issue. Most changes only come in when Joni requests additional data points or refinements. The bulk of the rework actually happens during retraining, which can take quite a long time, but that’s not due to problems with the code, rather it’s about improving and adapting to the underlying data at Avenews. 4 —
Quality Test coverage and automation maturity Avenews has implemented an agentic PR review process, which has been working really well. Most of the testing is currently done through scripts, with some parts still handled manually. 3 —
Quality Production incidents or escaped defects The risk model currently in production experienced some issues, not due to code, but rather misalignment around the variables used during testing. I make it a priority to ensure everything is properly validated and functioning as expected in production. 4 —
Quality Data classification and handling We’ve had Avenews-specific training on data policies and protections, and I apply those principles in all my work. I avoid pulling or processing sensitive data, and if it is present, I ensure it cannot be linked back to any individual or business. 4 —
Quality Access controls and environment segregation I currently have access to most of the data, as I work across the full data landscape within Avenews. There are separate environments for development, testing, and production, and I primarily work with production data. Access to modify data, such as editing tables, is controlled through least-privilege principles to limit unnecessary changes. 4 —
Quality Data storage and protection controls The team’s data is hosted on Amazon Web Services, where it is encrypted by default. In addition, all access to these data sources is secured through authentication controls. 5 —
Quality AI data usage controls I don’t upload any sensitive data during AI sessions, those are only used for troubleshooting and idea generation. At the moment, the data in our database isn’t masked, which does pose a risk. However, I’m working closely with the compliance officer, and we expect this to be addressed as part of the upcoming data governance framework. 4 —
Quality Breaches, near misses, or governance gaps This is an interesting area. Nothing like this has occurred since I joined the project, but there are clear guidelines and reporting processes in place should anything go wrong. It also highlights why we’re investing in data governance. 5 —
Quality Compliance confidence Avenews recently brought on a Compliance Officer, and I’ve been working closely with him on complaince and how that fits into data governance. He walked me through how we collect and use data, which gave me a much clearer understanding of the process. I have also focused on avoiding the use of sensitive data during model development and deployment. 4 —
Quality Quality Section Score — 4 Auto-calculated
Team Wellbeing Team morale My overall satisfaction is high. I’m happy where I am and value the growth and learning I’m gaining. 5 —
Team Wellbeing Workload sustainability The pace fluctuates quite a bit, when something urgent comes up, I shift focus to that. Outside of those moments, I’ve learned to manage my time effectively and stay on track. If any concerns around timelines arise, I’m comfortable discussing them with my team lead, who is supportive and helpful. 4 —
Team Wellbeing Burnout risk There are likely some signs of burnout, given the constant flow of tasks and ideas on the project. That said, I’ve adapted to the pace and have been able to stay on top of the work and continue delivering. 4 —
Team Wellbeing Team stability I’m currently the primary point of responsibility for all machine learning work on the project, as there isn’t a dedicated ML Engineer assigned. 3 —
Team Wellbeing Psychological safety and collaboration Yes, I have a very good relatiosnhip with the team lead and their CEO. These conversations have been happening and it has been well received 5 —
Team Wellbeing Team Wellbeing Section Score — 4.20 Auto-calculated
Customer Satisfaction & Engagement Customer sentiment Positive 5 Zander has set up bi-weekly check-ins with their lead to ensure alignment.
Customer Satisfaction & Engagement Customer engagement level — 4 —
Customer Satisfaction & Engagement Feedback received this period — 5 —
Customer Satisfaction & Engagement Responsiveness and collaboration — 3 —
Customer Satisfaction & Engagement Escalations or relationship strain — 4 —
Customer Satisfaction & Engagement Customer Satisfaction & Engagement Section Score — 4.20 Auto-calculated
Risk & Issue Management Active risks visibility Customer is responsible for risk management, our team make them aware of the risks or issues. No formal management in place within the customers environment. 1 —
Risk & Issue Management Active issues visibility Raised via informal channels such as word or mouth. 2 —
Risk & Issue Management Mitigation effectiveness Mitigations not always followed through. Slowly improving 2 —
Risk & Issue Management Critical risks or issues — 3 —
Risk & Issue Management Issue resolution responsiveness — 3 —
Risk & Issue Management Emerging risks / early warning signals Start up with typically react maturity levels, not good for high-impact projects 1 —
Risk & Issue Management Risk & Issue Management Section Score — 2 Auto-calculated
Funding Milestone Targets Funding milestones in scope? No — If applicable, describe milestones and current position.
Funding Milestone Targets Are milestone targets being met? — — —
Funding Milestone Targets Funding Milestone Targets Section Score / Rule Check — — If D82 = Yes and E83 <= 3, the Budget Health shown in the summary and overall weighted score is capped at 3 (Amber max).
AI Usage & Maturity Where is AI being used? Research, Upskilling and Personal Enablement to compliment delivery. Debugging in unfamiliar domains. — —
AI Usage & Maturity % of team actively using AI 1 — —
AI Usage & Maturity AI maturity level Experimental — —
AI Usage & Maturity Observed impact from AI Productivity, Speed — —
AI Usage & Maturity AI Usage & Maturity Section Score — — Auto-calculated
Tech Stack Used Current tech stack React (historically), MongoDB, Zoho Analytics, Python, Fast API, Swagger, Jupiter Notebooks, AWS — —
Tech Stack Used Material tech changes this period NA — —
Tech Stack Used Tech risks or constraints NA — —
Tech Stack Used Tech Stack Used Section Score — — Auto-calculated
New Opportunities Upsell / cross-sell opportunities — — —
New Opportunities Efficiency / delivery improvement opportunities — — —
New Opportunities Innovation or expansion opportunities — — —
New Opportunities New Opportunities Section Score — — Auto-calculated
B2B Shop Lead 178 rows
Section Question Response Score Notes
Context Client / Customer Account Cambridge University — —
Context Primary Focus for This Period First specs delivered to API team and they have already provided us the first Auth api's so we can start implementing and testing — —
Context Key Wins / Achievements Finally receiveing some of the API connections from the API team. — —
Context Biggest Challenges / Blockers Need a large pivot due to the pilot team pivoting. API team speed has picked up but still in the balance to reach Dec deadline — —
Context Major Changes (scope/team/budget/customer) Teams might merge pending our extension — —
Context Funding / Commercial Changes — — —
Context Single Most Important Leadership / Account Escalation — — —
Budget Health Budget type — — —
Budget Health Total budget vs utilised budget Slight underburn but under control 5 Budget is under hours.
Budget Health % of timeline elapsed vs % of budget consumed Slight underburn but under control 5 —
Budget Health Is spend aligned with progress? Yes 4 —
Budget Health Forecasted overrun or underrun risk Potential underrun 4 —
Budget Health Cost inefficiencies or rework impacting budget No 5 —
Budget Health Confidence in budget landing Medium, some risk for underburn but manageble 4 —
Budget Health Budget Health Section Score — 4.50 Auto-calculated
Timeline Health Key milestones on track Current timeline under pressure as this team is dependant on the APi team and they only started getting tracktion recently. I do believe now that we are starting to receive API's fromt eh API team we will start picking up speed. 2 —
Timeline Health % of planned work completed vs expected I think we have less than 5% of the required work completed but it should move faster from here 2 —
Timeline Health Schedule slippage this period There was a long delay for us to get the approved specs for the first few features, but now that we have it we are getting some traction 2 —
Timeline Health Dependencies impacting timelines Key man dependency on Toby to provide us with approved specs 2 —
Timeline Health Confidence in delivery dates even though we had a slow start I do feel we are picking up speed and should start delivering better from here on in 3 —
Timeline Health Timeline Health Section Score — 2.25 Auto-calculated
Delivery Health Consistency of delivery this period Seeing we only starting to get API from the API team now our delivery has been under pressure 2 —
Delivery Health Velocity trend Oor value flow (value delivered for the customer) Seeing we only starting to get API from the API team now our delivery has been under pressure 2 —
Delivery Health Customer value delivered Seeing we only starting to get API from the API team now our delivery has been under pressure 2 —
Delivery Health Flow bottlenecks or blockers Seeing we only starting to get API from the API team now our delivery has been under pressure 2 —
Delivery Health % of committed vs delivered work Seeing we only starting to get API from the API team now our delivery has been under pressure 2 —
Delivery Health Delivery Health Section Score — 2 Auto-calculated
Scope Health Scope clarity We struggled with this clarity previously but it really has improved over the last month 4 —
Scope Health Scope stability Some portion of the scope especially scope for later in the year is a little bit up in the air but continuously being discussed 3 —
Scope Health Change control effectiveness We have not experienced any change in scope over the last month 4 —
Scope Health Impact of scope changes on budget/timeline N/A 4 —
Scope Health Backlog health There is more than enough work to keep us busy until next year 5 —
Scope Health Scope Health Section Score — — Auto-calculated
Quality Engineering quality We have not deployed to prod as yet and only testing in Dev. But so far the quality seems to be good. We will get a better picture of quality once QA has been sorted. 4 —
Quality Compliance / data privacy / security This is managed by Cambridge and they are quite data sensitive with very strict security measures 4 Add details here
Quality Defect and rework trend We have not deployed to prod as yet and only testing in Dev. But so far the quality seems to be good. We will get a better picture of quality once QA has been sorted. 4 —
Quality Test coverage and automation maturity So for for where we are in the project I think this is good 4 —
Quality Production incidents or escaped defects N/A — —
Quality Data classification and handling This is managed by Cambridge and they are quite data sensitive with very strict security measures 5 Add details here
Quality Access controls and environment segregation This is managed by Cambridge and they are quite data sensitive with very strict security measures 5 —
Quality Data storage and protection controls This is managed by Cambridge and they are quite data sensitive with very strict security measures 5 —
Quality AI data usage controls We are following Synthesis standards 5 —
Quality Breaches, near misses, or governance gaps N/A — —
Quality Compliance confidence — 5 —
Quality Quality Section Score — 4.56 Auto-calculated
Team Wellbeing Team morale I think the team morale was a bit low the previous month with the uncertainty around timelines and scope, but I feel now that scope is clearer and we are starting to deliver items morale is also picking up 3 —
Team Wellbeing Workload sustainability We went from working but not being clear on what to do, to having a clear goal so I think this is under control 5 —
Team Wellbeing Burnout risk As it stands now I think burnout risk is low 5 —
Team Wellbeing Team stability Team stability seems good and I dont pick up any warning signs as yet 5 —
Team Wellbeing Psychological safety and collaboration — 5 —
Team Wellbeing Team Wellbeing Section Score — 4.60 Auto-calculated
Customer Satisfaction & Engagement Customer sentiment Seeing we are one piece in a larger puzzle this is a difficult question to ask. I think the client is nervous but more so because they struggled to get some traction and working with multiple teams and all affect each other. So for now I would say the client is nervous but more so because it took a long time for us to get going. So not so much negative towards us specifically but more so to the project 3 —
Customer Satisfaction & Engagement Customer engagement level The customer is very engaged 5 —
Customer Satisfaction & Engagement Feedback received this period No direct negative or positive feedback received as yet — —
Customer Satisfaction & Engagement Responsiveness and collaboration Collaboration between us and client is good and they are responsive even if it sometimes takes long to get things from them 4 —
Customer Satisfaction & Engagement Escalations or relationship strain I think this is getting better 3 —
Customer Satisfaction & Engagement Customer Satisfaction & Engagement Section Score — 3.75 Auto-calculated
Risk & Issue Management Active risks visibility The customer is responsible for managing the projects and we have been servicing risks. I do think we can do better in documenting and recording risks 3 —
Risk & Issue Management Active issues visibility The customer is responsible for managing the projects and we have been servicing risks. I do think we can do better in documenting and recording risks 3 —
Risk & Issue Management Mitigation effectiveness The customer does try and mitigate risk and so far have been effective in mitigating risks raised 4 —
Risk & Issue Management Critical risks or issues QA is a risk and also the speed of spec being done and approved. The uncertainty around the payment features is also a risk and might cause delays once we know complexity or system to use 2 —
Risk & Issue Management Issue resolution responsiveness When issues are experienced the client is quick to action but resolutions do take long because of red tape 2 —
Risk & Issue Management Emerging risks / early warning signals Timeline pressure is still a risk and we need to define the payment features and integrations as soon as possible 3 —
Risk & Issue Management Risk & Issue Management Section Score — 2.83 Auto-calculated
Funding Milestone Targets Funding milestones in scope? Yes — If applicable, describe milestones and current position.
Funding Milestone Targets Are milestone targets being met? — 4 —
Funding Milestone Targets Funding Milestone Targets Section Score / Rule Check — 4 If D82 = Yes and E83 <= 3, the Budget Health shown in the summary and overall weighted score is capped at 3 (Amber max).
AI Usage & Maturity Confirm with the customer if the use of AI is permitted for the project — — —
AI Usage & Maturity Where is AI being used? — — —
AI Usage & Maturity % of team actively using AI — — —
AI Usage & Maturity AI maturity level — — —
AI Usage & Maturity Observed impact from AI — — —
AI Usage & Maturity How are the team using AI on the project? — — —
AI Usage & Maturity AI Usage & Maturity Section Score — — Auto-calculated
Tech Stack Used Current tech stack — — —
Tech Stack Used Material tech changes this period — — —
Tech Stack Used Tech risks or constraints — — —
Tech Stack Used Tech Stack Used Section Score — — Auto-calculated
Project Compliance Does the project handle senstive data? — — —
Project Compliance What regulations must be complied with — — —

Showing first 80 of 178 commentary rows for this project.

Comprehensive Internal Project Health Report_v0.01_ShareForce_June_2026 89 rows
Section Question Response Score Notes
Context Client / Customer Account Capture the client or account name for account-level risk reporting. — —
Context Primary Focus for This Period Feature Development: Stakeholder Access Control, Secuirty Classes and Share Ledger — —
Context Key Wins / Achievements Module Completion: Successfully delivered the full Stakeholder Management and Security Classes modules (Common, Preferred, and Convertible instruments) Share Ledger Milestone: Near-total delivery of the Share Ledger module, including browser views, warrant grants, and the ability to reverse/edit complex transactions UI/UX Redesign: Implemented a brighter, brand-aligned navigation side panel and a Settings page that propagates base currency selections system-wide Test Automation Maturity: Optimized the Cypress testing suite by implementing session and cookie reuse, which significantly increased the speed and efficiency of automated "happy path" verification AWS Integration: Finalized AWS S3 integration, enabling secure document management for shareholder and security instrument records — —
Context Biggest Challenges / Blockers None — —
Context Major Changes (scope/team/budget/customer) None — —
Context Funding / Commercial Changes N/A — —
Context Single Most Important Leadership / Account Escalation None — —
Budget Health Budget type Fixed Price — —
Budget Health Total budget vs utilised budget R 3,108,240.00/ R 529 390,00 = 17% 4 —
Budget Health % of timeline elapsed vs % of budget consumed 0.33 5 —
Budget Health Is spend aligned with progress? Ahead 5 —
Budget Health Forecasted overrun or underrun risk Overrun 5 —
Budget Health Cost inefficiencies or rework impacting budget None 5 —
Budget Health Confidence in budget landing Strong 5 —
Budget Health Budget Health Section Score — 4.83 Auto-calculated
Timeline Health Key milestones on track On track 5 —
Timeline Health % of planned work completed vs expected 0.33 5 —
Timeline Health Schedule slippage this period Ahead of schedule 5 —
Timeline Health Dependencies impacting timelines No dependecies 5 —
Timeline Health Confidence in delivery dates High 5 —
Timeline Health Timeline Health Section Score — 5 Auto-calculated
Delivery Health Consistency of delivery this period Yes, the team has consistantly over 3 sprints, above 30 points 5 —
Delivery Health Velocity trend Oor value flow (value delivered for the customer) velocity is 33 points 5 —
Delivery Health Customer value delivered Stakeholder Access Control, Secuirty Classes and Share Ledger 5 —
Delivery Health Flow bottlenecks or blockers None 5 —
Delivery Health % of committed vs delivered work Over delivered 118% 5 —
Delivery Health Delivery Health Section Score — 5 Auto-calculated
Scope Health Scope clarity Everyone has clear understanding of requirements 5 —
Scope Health Scope stability Exteremly stable 5 —
Scope Health Change control effectiveness Changes are logged as bugs, enhancements tickets 5 —
Scope Health Impact of scope changes on budget/timeline None, the team is ahead of schedule 5 —
Scope Health Backlog health No comment, backlog is well groomed and maintained 5 —
Scope Health Scope Health Section Score — 5 Auto-calculated
Quality Engineering quality I have given a lower score, as we have logged a significant number of bugs, but these are small bugs, and because QA and development are happening in the same environment (QA) 4 —
Quality Compliance / data privacy / security The assessment is still in progress. At present, there is no immediate risk, as the team is not yet working with live client data. 4 The assessment is still in progress. At present, there is no immediate risk, as the team is not yet working with live client data.However, it is important to note that appropriate controls and measures will need to be implemented before the introduction of real client data. This is due to the sensitive nature of the information, which includes critical financial data. Ensuring proper data protection, security, and compliance mechanisms will be essential prior to proceeding.
Quality Defect and rework trend I have given a lower score, as we have logged a significant number of bugs, but these are small bugs, and because QA and development are happening in the same environment (QA) 4 —
Quality Test coverage and automation maturity Unit, automated, and manual testing are currently being implemented. 5 —
Quality Production incidents or escaped defects Not in Production yet 5 —
Quality Data classification and handling Critical data, financial data 4 Team is in the progress of implementing the correct secuirty and complaince for critical PIIA data
Quality Access controls and environment segregation Aim is secure login, SSO and 2FA, it is still in the progress of being developed 4 —
Quality Data storage and protection controls In progress, we are still in development. 4 —
Quality AI data usage controls Using mocked data, currently, no access to actual client data 5 —
Quality Breaches, near misses, or governance gaps None 5 —
Quality Compliance confidence Medium, still in development 4 —
Quality Quality Section Score — 4.36 Auto-calculated
Team Wellbeing Team morale All healthy, no concerns 5 —
Team Wellbeing Workload sustainability All healthy, no concerns 5 —
Team Wellbeing Burnout risk All healthy, no concerns 5 —
Team Wellbeing Team stability All healthy, no concerns 5 —
Team Wellbeing Psychological safety and collaboration All healthy, no concerns 5 —
Team Wellbeing Team Wellbeing Section Score — 5 Auto-calculated
Customer Satisfaction & Engagement Customer sentiment Only positive feedback 5 —
Customer Satisfaction & Engagement Customer engagement level High participation 5 —
Customer Satisfaction & Engagement Feedback received this period Jason — Big shoutout for mastering the Share Ledger so well. At this point, Share Ledger probably asks Jason for approval before doing anything. Arpit — Well done for kicking off automation testing. This is going to be extremely helpful and will make testing much smoother going forward. Michaela — Thanks for always being ready to brainstorm through a problem statement. Those discussions really help us get to better solutions as a team. Jessica — As always, thank you for handling everything so well and keeping things moving calmly and clearly for the team. 5 —
Customer Satisfaction & Engagement Responsiveness and collaboration High communication 5 —
Customer Satisfaction & Engagement Escalations or relationship strain None 5 —
Customer Satisfaction & Engagement Customer Satisfaction & Engagement Section Score — 5 Auto-calculated
Risk & Issue Management Active risks visibility No risks 5 —
Risk & Issue Management Active issues visibility N/A 5 —
Risk & Issue Management Mitigation effectiveness N/A 5 —
Risk & Issue Management Critical risks or issues N/A 5 —
Risk & Issue Management Issue resolution responsiveness N/A 5 —
Risk & Issue Management Emerging risks / early warning signals N/A 5 —
Risk & Issue Management Risk & Issue Management Section Score — 5 Auto-calculated
Funding Milestone Targets Funding milestones in scope? No — If applicable, describe milestones and current position.
Funding Milestone Targets Are milestone targets being met? No 4 —
Funding Milestone Targets Funding Milestone Targets Section Score / Rule Check — 4 If D82 = Yes and E83 <= 3, the Budget Health shown in the summary and overall weighted score is capped at 3 (Amber max).
AI Usage & Maturity Confirm with the customer if the use of AI is permitted for the project Yes permitted — —
AI Usage & Maturity Where is AI being used? Full development used with AI — —
AI Usage & Maturity % of team actively using AI 1 — —
AI Usage & Maturity AI maturity level Optimised — —
AI Usage & Maturity Observed impact from AI Yes delivery is much faster, the team is ahead of schedule — —
AI Usage & Maturity How are the team using AI on the project? Full-stack development is being carried out with AI (Cluad), with AI also being leveraged for testing, project status reporting, and test case development. — —
AI Usage & Maturity AI Usage & Maturity Section Score — — Auto-calculated
Tech Stack Used Current tech stack Python, Django, Postgress, Redis, Heroku, AWS S3 — —
Tech Stack Used Material tech changes this period None — —
Tech Stack Used Tech risks or constraints None — —
Tech Stack Used Tech Stack Used Section Score — — Auto-calculated
Project Compliance Does the project handle senstive data? Yes, it plans to, but not currently — —
Project Compliance What regulations must be complied with POPIA & GDPR — —

Showing first 80 of 89 commentary rows for this project.

Comprehensive Internal Project Health Report_v0.011 89 rows
Section Question Response Score Notes
Context Client / Customer Account Capture the client or account name for account-level risk reporting. — —
Context Primary Focus for This Period — — —
Context Key Wins / Achievements — — —
Context Biggest Challenges / Blockers — — —
Context Major Changes (scope/team/budget/customer) — — —
Context Funding / Commercial Changes — — —
Context Single Most Important Leadership / Account Escalation — — —
Budget Health Budget type — — —
Budget Health Total budget vs utilised budget — 5 —
Budget Health % of timeline elapsed vs % of budget consumed — 5 —
Budget Health Is spend aligned with progress? — 5 —
Budget Health Forecasted overrun or underrun risk — 5 —
Budget Health Cost inefficiencies or rework impacting budget — 5 —
Budget Health Confidence in budget landing — 5 —
Budget Health Budget Health Section Score — 5 Auto-calculated
Timeline Health Key milestones on track — — —
Timeline Health % of planned work completed vs expected — — —
Timeline Health Schedule slippage this period — — —
Timeline Health Dependencies impacting timelines — — —
Timeline Health Confidence in delivery dates — — —
Timeline Health Timeline Health Section Score — — Auto-calculated
Delivery Health Consistency of delivery this period — — Audit documentation is underway currently. Pipelines are a work in progress to enable an automated deployment.
Delivery Health Velocity trend Oor value flow (value delivered for the customer) — — —
Delivery Health Customer value delivered — — —
Delivery Health Flow bottlenecks or blockers — — It is unclear how clients will connect with the clients networking team. Unknowns must be worked through with this team. This requires input from clients. This will define future client requirements
Delivery Health % of committed vs delivered work — — —
Delivery Health Delivery Health Section Score — — Auto-calculated
Scope Health Scope clarity New features will be included (e.g. Oauth) Integration work Any challenges from deployments — Audit underway at the moment (MWR) which will determine when we can make a deployment.
Scope Health Scope stability — — —
Scope Health Change control effectiveness — — —
Scope Health Impact of scope changes on budget/timeline — — —
Scope Health Backlog health — — —
Scope Health Scope Health Section Score — — Auto-calculated
Quality Engineering quality — — —
Quality Compliance / data privacy / security — — Add details here
Quality Defect and rework trend — — —
Quality Test coverage and automation maturity — — —
Quality Production incidents or escaped defects — — —
Quality Data classification and handling — — Add details here
Quality Access controls and environment segregation — — —
Quality Data storage and protection controls — — —
Quality AI data usage controls — — —
Quality Breaches, near misses, or governance gaps — — —
Quality Compliance confidence — — —
Quality Quality Section Score — — Auto-calculated
Team Wellbeing Team morale — — —
Team Wellbeing Workload sustainability — — —
Team Wellbeing Burnout risk — — —
Team Wellbeing Team stability — — —
Team Wellbeing Psychological safety and collaboration — — —
Team Wellbeing Team Wellbeing Section Score — — Auto-calculated
Customer Satisfaction & Engagement Customer sentiment — — —
Customer Satisfaction & Engagement Customer engagement level — — —
Customer Satisfaction & Engagement Feedback received this period — — —
Customer Satisfaction & Engagement Responsiveness and collaboration — — —
Customer Satisfaction & Engagement Escalations or relationship strain — — —
Customer Satisfaction & Engagement Customer Satisfaction & Engagement Section Score — — Auto-calculated
Risk & Issue Management Active risks visibility — — —
Risk & Issue Management Active issues visibility — — —
Risk & Issue Management Mitigation effectiveness — — —
Risk & Issue Management Critical risks or issues — — —
Risk & Issue Management Issue resolution responsiveness — — —
Risk & Issue Management Emerging risks / early warning signals — — —
Risk & Issue Management Risk & Issue Management Section Score — — Auto-calculated
Funding Milestone Targets Funding milestones in scope? Yes — If applicable, describe milestones and current position.
Funding Milestone Targets Are milestone targets being met? — 4 —
Funding Milestone Targets Funding Milestone Targets Section Score / Rule Check — 4 If D82 = Yes and E83 <= 3, the Budget Health shown in the summary and overall weighted score is capped at 3 (Amber max).
AI Usage & Maturity Confirm with the customer if the use of AI is permitted for the project — — —
AI Usage & Maturity Where is AI being used? — — —
AI Usage & Maturity % of team actively using AI — — —
AI Usage & Maturity AI maturity level — — —
AI Usage & Maturity Observed impact from AI — — —
AI Usage & Maturity How are the team using AI on the project? — — —
AI Usage & Maturity AI Usage & Maturity Section Score — — Auto-calculated
Tech Stack Used Current tech stack — — —
Tech Stack Used Material tech changes this period — — —
Tech Stack Used Tech risks or constraints — — —
Tech Stack Used Tech Stack Used Section Score — — Auto-calculated
Project Compliance Does the project handle senstive data? — — —
Project Compliance What regulations must be complied with — — —

Showing first 80 of 89 commentary rows for this project.

Credeq - Data Platform 80 rows
Section Question Response Score Notes
Context Client / Customer Account Credeq — —
Context Primary Focus for This Period Continued data ingression and transformation — —
Context Key Wins / Achievements Data platform: Commbonds Australia phase 1 deployed to prod (ingestion into bronze) — —
Context Biggest Challenges / Blockers Data platform: Ongoing data work for a non-data team — —
Context Major Changes (scope/team/budget/customer) Data platform: None to report — —
Context Funding / Commercial Changes None in this timeframe — —
Context Single Most Important Leadership / Account Escalation None at this time — —
Budget Health Budget type — — —
Budget Health Total budget vs utilised budget 49,3% planned vs 29,5% actual 4 Budget underburnt
Budget Health % of timeline elapsed vs % of budget consumed — 4 —
Budget Health Is spend aligned with progress? Actual budget tracking behind project progress 4 Delivering what we should, but with less hours
Budget Health Forecasted overrun or underrun risk Forecast is continued underburn 4 —
Budget Health Cost inefficiencies or rework impacting budget Onboarding of team before all data was ready, phased availability of data sources. 4 —
Budget Health Confidence in budget landing High 4 We have underburning every month
Budget Health Budget Health Section Score — 4 Auto-calculated
Timeline Health Key milestones on track No missed milestones so far 5 —
Timeline Health % of planned work completed vs expected ShapeUp does not track work this way 5 —
Timeline Health Schedule slippage this period — 5 —
Timeline Health Dependencies impacting timelines — 4 —
Timeline Health Confidence in delivery dates High 4 Things can change quickly, but confident in terms of the latest planning
Timeline Health Timeline Health Section Score — 4.60 Auto-calculated
Delivery Health Consistency of delivery this period Sporadic bursts of delivery in past, but now stabilising 3 Some blockers and learning was required, but trending better
Delivery Health Velocity trend Stable 4 —
Delivery Health Customer value delivered Yes 4 Difficult to quantify due to the nature of the project
Delivery Health Flow bottlenecks or blockers — 4 —
Delivery Health % of committed vs delivered work — 5 —
Delivery Health Delivery Health Section Score — 4 Auto-calculated
Scope Health Scope clarity Some gaps in the overall picture that has been partially resolved 3 Scope got clearer from first cycle to the second, working towards long term roadmap clarity
Scope Health Scope stability — 4 Within normal expectation
Scope Health Change control effectiveness — 5 —
Scope Health Impact of scope changes on budget/timeline — 5 —
Scope Health Backlog health — 5 —
Scope Health Scope Health Section Score — 4.40 Auto-calculated
Quality Engineering quality — 5 —
Quality Compliance / data privacy / security The project deals with data from insurance sources. High risk but currently compliant 5 Governance from multiple regions (Aus, EU, Africa)
Quality Defect and rework trend — 5 —
Quality Test coverage and automation maturity — 5 —
Quality Production incidents or escaped defects — 5 —
Quality Data classification and handling — 5 —
Quality Access controls and environment segregation — 5 —
Quality Data storage and protection controls — 5 —
Quality AI data usage controls — 5 —
Quality Breaches, near misses, or governance gaps — 5 —
Quality Compliance confidence — 5 —
Quality Quality Section Score — 5 Auto-calculated
Team Wellbeing Team morale Team morale is trending better but still being monitored. 4 Previous low morale issues improved
Team Wellbeing Workload sustainability The pace is sustainable, but the nature of the project might impact morale 5 Repetitive work and lack of big picture view might impact the team going forward
Team Wellbeing Burnout risk — 4 —
Team Wellbeing Team stability Severe key person risk for GCP skills 4 This issue is already known and monitored by management
Team Wellbeing Psychological safety and collaboration Team has been speaking up more regularly and voicing their concerns more openly 4 —
Team Wellbeing Team Wellbeing Section Score — 4.20 Auto-calculated
Customer Satisfaction & Engagement Customer sentiment Good relationships and no compliants about the Synthesis work or team 5 —
Customer Satisfaction & Engagement Customer engagement level Active, they have a project team providing direction 5 —
Customer Satisfaction & Engagement Feedback received this period No formal feedback from customer 4 —
Customer Satisfaction & Engagement Responsiveness and collaboration Great dependency on customer internal workings and stakeholders 4 Credeq project team is managing these issues
Customer Satisfaction & Engagement Escalations or relationship strain — 5 —
Customer Satisfaction & Engagement Customer Satisfaction & Engagement Section Score — 4.60 Auto-calculated
Risk & Issue Management Active risks visibility We do not have a central project risk tracker 3 —
Risk & Issue Management Active issues visibility Many of the issues sit with the customer team. 3 —
Risk & Issue Management Mitigation effectiveness — 5 —
Risk & Issue Management Critical risks or issues No crititical risks 5 —
Risk & Issue Management Issue resolution responsiveness — 5 —
Risk & Issue Management Emerging risks / early warning signals — 5 —
Risk & Issue Management Risk & Issue Management Section Score — 4.33 Auto-calculated
Funding Milestone Targets Funding milestones in scope? No — If applicable, describe milestones and current position.
Funding Milestone Targets Are milestone targets being met? — — —
Funding Milestone Targets Funding Milestone Targets Section Score / Rule Check — — If D82 = Yes and E83 <= 3, the Budget Health shown in the summary and overall weighted score is capped at 3 (Amber max).
AI Usage & Maturity Where is AI being used? General development and research — —
AI Usage & Maturity % of team actively using AI 0.8 — —
AI Usage & Maturity AI maturity level Embedded — —
AI Usage & Maturity Observed impact from AI Limited observational impact — —
AI Usage & Maturity AI Usage & Maturity Section Score — — Auto-calculated
Tech Stack Used Current tech stack — — —
Tech Stack Used Material tech changes this period — — —
Tech Stack Used Tech risks or constraints — — —
Tech Stack Used Tech Stack Used Section Score — — Auto-calculated
New Opportunities Upsell / cross-sell opportunities Ongoing, expanding programme — —
New Opportunities Efficiency / delivery improvement opportunities None identified — —
New Opportunities Innovation or expansion opportunities Project naturally expands as new work and data sources becomes available from customer — New resources not required for new data/work at this time
New Opportunities New Opportunities Section Score — — Auto-calculated
Credeq - Guarantee Gateway 249 rows
Section Question Response Score Notes
Context Client / Customer Account Credeq Guarantee Gateway — —
Context Primary Focus for This Period project set-up, onboarding, planning and kick-off first sprint — —
Context Key Wins / Achievements • Construction Guarantees: BRS completed and conditionally signed off. • Architecture: Agreement reached on a decoupled wrapper API with an anti corruption layer; Marshall platform walkthrough completed. • Identity: Decision made to adopt Auth0, enabling MFA and modern authentication capabilities. • Delivery Enablement: GitHub was implemented for source control and project management. • Development: Build activities have commenced, focusing on reusable UI components and the Construction landing page. • API Discovery: Gap analysis identified missing Marshall API capabilities required for the new UI designs; follow up workshops are planned. — —
Context Biggest Challenges / Blockers There is a risk to the timely completion of the Mining UI designs and Business Requirements Specification (BRS) due to outstanding and delayed business inputs. While Trade Guarantees onboarding has commenced, the full requirements for the end to end online application are still to be uncovered. As a result, the remaining design capacity is insufficient to fully complete both Mining and Trade within the available timeframe. — —
Context Major Changes (scope/team/budget/customer) Additional resource add to the project. Leandre Roux. — —
Context Funding / Commercial Changes n/a — —
Context Single Most Important Leadership / Account Escalation — — —
Budget Health Budget type Fixed Price — —
Budget Health Total budget vs utilised budget On budget 5 —
Budget Health % of timeline elapsed vs % of budget consumed Alings 5 —
Budget Health Is spend aligned with progress? Yes 5 —
Budget Health Forecasted overrun or underrun risk Currently on budget 5 —
Budget Health Cost inefficiencies or rework impacting budget n/a 5 —
Budget Health Confidence in budget landing high 5 —
Budget Health Budget Health Section Score — 5 Auto-calculated
Timeline Health Key milestones on track On track 5 —
Timeline Health % of planned work completed vs expected On track 5 —
Timeline Health Schedule slippage this period On track 5 —
Timeline Health Dependencies impacting timelines On track 5 —
Timeline Health Confidence in delivery dates High 5 —
Timeline Health Timeline Health Section Score — 5 Auto-calculated
Delivery Health Consistency of delivery this period On track 5 —
Delivery Health Velocity trend On track 5 —
Delivery Health Customer value delivered — 5 —
Delivery Health Flow bottlenecks or blockers — 5 —
Delivery Health % of committed vs delivered work — 5 —
Delivery Health Delivery Health Section Score — 5 Auto-calculated
Scope Health Scope clarity — 5 —
Scope Health Scope stability — 4 —
Scope Health Change control effectiveness — 5 —
Scope Health Impact of scope changes on budget/timeline — 4 —
Scope Health Backlog health — 5 —
Scope Health Scope Health Section Score — 4.60 Auto-calculated
Quality Engineering quality — 5 —
Quality Compliance / data privacy / security — 5 —
Quality Defect and rework trend — 5 —
Quality Test coverage and automation maturity Not applicable just yet, we only started development last week. 5 —
Quality Production incidents or escaped defects — 5 —
Quality Data classification and handling — 5 —
Quality Access controls and environment segregation Yes, the solution will have Dev, UAT and Prod Segragation 5 —
Quality Data storage and protection controls Archietcture wise data storage would be by the 3rd party, the FE app would not persist data. 5 —
Quality AI data usage controls — 5 —
Quality Breaches, near misses, or governance gaps — 5 —
Quality Compliance confidence Medium, further investigation is required 5 —
Quality Quality Section Score — 5 Auto-calculated
Team Wellbeing Team morale — 5 —
Team Wellbeing Workload sustainability — 5 —
Team Wellbeing Burnout risk — 5 —
Team Wellbeing Team stability — 5 —
Team Wellbeing Psychological safety and collaboration — 5 —
Team Wellbeing Team Wellbeing Section Score — 5 Auto-calculated
Customer Satisfaction & Engagement Customer sentiment — 5 —
Customer Satisfaction & Engagement Customer engagement level — 5 —
Customer Satisfaction & Engagement Feedback received this period — 5 —
Customer Satisfaction & Engagement Responsiveness and collaboration — 5 —
Customer Satisfaction & Engagement Escalations or relationship strain — 5 —
Customer Satisfaction & Engagement Customer Satisfaction & Engagement Section Score — 5 Auto-calculated
Risk & Issue Management Active risks visibility — 5 —
Risk & Issue Management Active issues visibility — 5 —
Risk & Issue Management Mitigation effectiveness — 4 —
Risk & Issue Management Critical risks or issues — 5 —
Risk & Issue Management Issue resolution responsiveness — 5 —
Risk & Issue Management Emerging risks / early warning signals — 5 —
Risk & Issue Management Risk & Issue Management Section Score — 4.83 Auto-calculated
Funding Milestone Targets Funding milestones in scope? Yes — If applicable, describe milestones and current position.
Funding Milestone Targets Are milestone targets being met? n/a 4 —
Funding Milestone Targets Funding Milestone Targets Section Score / Rule Check — 4 If D82 = Yes and E83 <= 3, the Budget Health shown in the summary and overall weighted score is capped at 3 (Amber max).
AI Usage & Maturity Where is AI being used? No AI being utilized 0 —
AI Usage & Maturity % of team actively using AI No AI being utilized 0 —
AI Usage & Maturity AI maturity level None 0 —
AI Usage & Maturity Observed impact from AI n/a 0 —
AI Usage & Maturity AI Usage & Maturity Section Score — — Auto-calculated
Tech Stack Used Current tech stack Azure, Terraform, C#, Angular, GitHub Actions, Yaml. Possibly to include 3rd party once we have finalised Oaut — —
Tech Stack Used Material tech changes this period Moving away from IaaS (VM & IIS) and moving to PaaS such as Web Apps and Containerisation — —
Tech Stack Used Tech risks or constraints No risks has been identified — —
Tech Stack Used Tech Stack Used Section Score — — Auto-calculated
New Opportunities Upsell / cross-sell opportunities — — —
New Opportunities Efficiency / delivery improvement opportunities — — —
New Opportunities Innovation or expansion opportunities — — —
New Opportunities New Opportunities Section Score — — Auto-calculated

Showing first 80 of 249 commentary rows for this project.

English Design System (EDS) 178 rows
Section Question Response Score Notes
Context Client / Customer Account Cambridge University — —
Context Primary Focus for This Period Finalise core components and now focusing on change request and updates to components — —
Context Key Wins / Achievements Completed the core components and pushed to prod — —
Context Biggest Challenges / Blockers The Manila team is very slow which is a risk to be raised. We also need a full time design person. This has been raised with Cambridge — —
Context Major Changes (scope/team/budget/customer) TPO (Tech Product Owner) has left, being picked up by the Scrum Master. No other major changes have happened. — —
Context Funding / Commercial Changes — — —
Context Single Most Important Leadership / Account Escalation Waiting on confirmation of extension — —
Budget Health Budget type — — —
Budget Health Total budget vs utilised budget There is a slight underburn but nothing concerning 5 —
Budget Health % of timeline elapsed vs % of budget consumed There is a slight underburn but nothing concerning 5 —
Budget Health Is spend aligned with progress? Yes 5 —
Budget Health Forecasted overrun or underrun risk Potential underrun 4 —
Budget Health Cost inefficiencies or rework impacting budget No 5 —
Budget Health Confidence in budget landing Strong confidence it will land within budget 5 —
Budget Health Budget Health Section Score — 4.75 Auto-calculated
Timeline Health Key milestones on track We are on track. We delivered the core components and in a position where we can pick up changes and deploy them quickly. We are currently deploying once a week. 5 —
Timeline Health % of planned work completed vs expected Completed all planned work 5 —
Timeline Health Schedule slippage this period No deadlines missed 5 —
Timeline Health Dependencies impacting timelines Design team and Manila team causing some delays but we are still on track. We need to figure out most of the technical items and pull the requirements out of team. 4 —
Timeline Health Confidence in delivery dates We dont manage the delivery dates, but as far as we know we are still on track 4 —
Timeline Health Timeline Health Section Score — 4.50 Auto-calculated
Delivery Health Consistency of delivery this period Consistent. Currently doing releases once a week to prod 5 —
Delivery Health Velocity trend Oor value flow (value delivered for the customer) Velocity is good, but can definitely be better if we sort the Design delays and Manila team delays 4 —
Delivery Health Customer value delivered Customers prioritise items so difficult to determine if we deliver value. But we have delivered all the prioritised items 4 —
Delivery Health Flow bottlenecks or blockers No big blockers 5 —
Delivery Health % of committed vs delivered work We have delivered all planned work 5 —
Delivery Health Delivery Health Section Score — 4.60 Auto-calculated
Scope Health Scope clarity Roadmap is a little unclear, but when it comes to delivering components it is clear what that component needs to do 4 —
Scope Health Scope stability Stable 5 —
Scope Health Change control effectiveness Client determines priority so they also manage the change process 5 —
Scope Health Impact of scope changes on budget/timeline No real impact as this is more an augmentation project 5 —
Scope Health Backlog health Backlog is stable 5 —
Scope Health Scope Health Section Score — — Auto-calculated
Quality Engineering quality Quality can be improved. The main focus was on getting the work out first, which caused some tech debt. But we are currently working on stabilising 4 —
Quality Compliance / data privacy / security N/A — Add details here
Quality Defect and rework trend There has been a bit of rework after our deployment but it was planned 4 —
Quality Test coverage and automation maturity There is significant test coverage in the project. Both automated and functional and we have to achieve above 80% for PR to pass. 5 —
Quality Production incidents or escaped defects One or two big items came back from Prod but all manageble 4 —
Quality Data classification and handling N/A — Add details here
Quality Access controls and environment segregation N/A — This is managed by Cambridge
Quality Data storage and protection controls N/A — —
Quality AI data usage controls N/A — —
Quality Breaches, near misses, or governance gaps N/A — —
Quality Compliance confidence N/A — —
Quality Quality Section Score — 4.25 Auto-calculated
Team Wellbeing Team morale Morale is a bit low but the team can do some work to build better relationships with the other dev teams specifically Manila team 3 —
Team Wellbeing Workload sustainability Workload for designer is way too much and they need some help. Dev workload is manageable 3 —
Team Wellbeing Burnout risk For designer yes. Henko doing most of the work in Dev so needs to be monitored as he is picking up much more than the other (Manila) devs 3 —
Team Wellbeing Team stability Stable 5 —
Team Wellbeing Psychological safety and collaboration — 5 —
Team Wellbeing Team Wellbeing Section Score — 3.80 Auto-calculated
Customer Satisfaction & Engagement Customer sentiment — — —
Customer Satisfaction & Engagement Customer engagement level — — —
Customer Satisfaction & Engagement Feedback received this period — — —
Customer Satisfaction & Engagement Responsiveness and collaboration — — —
Customer Satisfaction & Engagement Escalations or relationship strain — — —
Customer Satisfaction & Engagement Customer Satisfaction & Engagement Section Score — — Auto-calculated
Risk & Issue Management Active risks visibility Cambridge manages this project and risks, but we do continuously raise any risks we pick up. 5 —
Risk & Issue Management Active issues visibility Cambridge manages this project and risks, but we do continuously raise any risks we pick up. 5 —
Risk & Issue Management Mitigation effectiveness — 5 —
Risk & Issue Management Critical risks or issues As discussed the design workload is a risk 5 —
Risk & Issue Management Issue resolution responsiveness Actively tracked and reviewed once every two weeks 5 —
Risk & Issue Management Emerging risks / early warning signals Maybe keep an eye on Henko 5 —
Risk & Issue Management Risk & Issue Management Section Score — 5 Auto-calculated
Funding Milestone Targets Funding milestones in scope? Yes — If applicable, describe milestones and current position.
Funding Milestone Targets Are milestone targets being met? — 4 —
Funding Milestone Targets Funding Milestone Targets Section Score / Rule Check — 4 If D82 = Yes and E83 <= 3, the Budget Health shown in the summary and overall weighted score is capped at 3 (Amber max).
AI Usage & Maturity Confirm with the customer if the use of AI is permitted for the project There is no specific policy at Cambridge that we are aware of but it is encouraged that AI tools be used to get more efficient — —
AI Usage & Maturity Where is AI being used? Jira management and Development — —
AI Usage & Maturity % of team actively using AI Only Henko. The Manila team are not or we are not sure on them — —
AI Usage & Maturity AI maturity level Embedded — —
AI Usage & Maturity Observed impact from AI Faster development — —
AI Usage & Maturity How are the team using AI on the project? Requirements, Dev assistance, testing — —
AI Usage & Maturity AI Usage & Maturity Section Score — — Auto-calculated
Tech Stack Used Current tech stack React, NX, SCSS — —
Tech Stack Used Material tech changes this period N/A — —
Tech Stack Used Tech risks or constraints Nothing critical at the moment — —
Tech Stack Used Tech Stack Used Section Score — — Auto-calculated
Project Compliance Does the project handle senstive data? No — —
Project Compliance What regulations must be complied with N/A — —

Showing first 80 of 178 commentary rows for this project.

Lombard - Cloud M 80 rows
Section Question Response Score Notes
Context Client / Customer Account Lombard — —
Context Primary Focus for This Period Migrate 6TB from Dropbox to Goodle Workspace uising CloudM — —
Context Key Wins / Achievements Kick off is done, onboarding access granted to team, CloudM licencing procured — —
Context Biggest Challenges / Blockers Not currently — —
Context Major Changes (scope/team/budget/customer) N/A — —
Context Funding / Commercial Changes N/A — —
Context Single Most Important Leadership / Account Escalation N/A — —
Budget Health Budget type Fixed Price — Budget tracking well for April
Budget Health Total budget vs utilised budget On budget 5 —
Budget Health % of timeline elapsed vs % of budget consumed Aligns 4 Slightly delayed start results in utilisation gap. This will be shown in the revenue earned for the month of April as a decrease due to billing only commencing from May.
Budget Health Is spend aligned with progress? Yes 5 —
Budget Health Forecasted overrun or underrun risk Currently on budget 5 —
Budget Health Cost inefficiencies or rework impacting budget n/a 5 —
Budget Health Confidence in budget landing High 5 —
Budget Health Budget Health Section Score — 4.83 Auto-calculated
Timeline Health Key milestones on track On track 5 —
Timeline Health % of planned work completed vs expected On track 5 —
Timeline Health Schedule slippage this period On track 5 —
Timeline Health Dependencies impacting timelines N/A 5 —
Timeline Health Confidence in delivery dates Overall high 5 —
Timeline Health Timeline Health Section Score — 5 Auto-calculated
Delivery Health Consistency of delivery this period On track 5 —
Delivery Health Velocity trend On track 5 —
Delivery Health Customer value delivered On track to do so 5 —
Delivery Health Flow bottlenecks or blockers N/A 5 —
Delivery Health % of committed vs delivered work On track 5 —
Delivery Health Delivery Health Section Score — 5 Auto-calculated
Scope Health Scope clarity Yes 5 —
Scope Health Scope stability No 5 —
Scope Health Change control effectiveness Yes when applicable 5 —
Scope Health Impact of scope changes on budget/timeline To be managed if applicable 5 —
Scope Health Backlog health Refined 5 —
Scope Health Scope Health Section Score — 5 Auto-calculated
Quality Engineering quality Team quality is high 5 —
Quality Compliance / data privacy / security In line with Lombards security standards 5 —
Quality Defect and rework trend — 5 —
Quality Test coverage and automation maturity — 5 —
Quality Production incidents or escaped defects — 5 —
Quality Data classification and handling — 5 —
Quality Access controls and environment segregation Access controls run by Lombard Infra and SecOps 5 —
Quality Data storage and protection controls In line with Lombards security standards 5 —
Quality AI data usage controls In line with Lombards security standards 5 Teams utilize AI tools fequently. Lombard is an AI first org. Gemini tools mostly in play. Cursor discussions underway
Quality Breaches, near misses, or governance gaps Not applicable to Synthesis actions in April 5 —
Quality Compliance confidence High 5 —
Quality Quality Section Score — 5 Auto-calculated
Team Wellbeing Team morale High 5 —
Team Wellbeing Workload sustainability Early stages - too early to say 5 —
Team Wellbeing Burnout risk Monitoring 4 —
Team Wellbeing Team stability Stable 5 —
Team Wellbeing Psychological safety and collaboration Regular team meetings and check ins in play. Customer is extremely considerate to team needs as well 5 —
Team Wellbeing Team Wellbeing Section Score — 5 Auto-calculated
Customer Satisfaction & Engagement Customer sentiment Positive 5 —
Customer Satisfaction & Engagement Customer engagement level Regular, fair and transaprent. Inclusive communication consistently 5 —
Customer Satisfaction & Engagement Feedback received this period Consistent positive feedback towards our team. Call outs noted and communicated 5 —
Customer Satisfaction & Engagement Responsiveness and collaboration Overall high engagement 5 —
Customer Satisfaction & Engagement Escalations or relationship strain Not currently 5 —
Customer Satisfaction & Engagement Customer Satisfaction & Engagement Section Score — 5 Auto-calculated
Risk & Issue Management Active risks visibility Early identification regularly - tracked and managed 5 —
Risk & Issue Management Active issues visibility All issues are owned 5 —
Risk & Issue Management Mitigation effectiveness Yes 5 —
Risk & Issue Management Critical risks or issues No critical risks currently 5 —
Risk & Issue Management Issue resolution responsiveness To date, issues handled quickly 5 —
Risk & Issue Management Emerging risks / early warning signals Mostly early idenfication 5 Risks are noted early and managed
Risk & Issue Management Risk & Issue Management Section Score — 5 Auto-calculated
Funding Milestone Targets Funding milestones in scope? — — If applicable, describe milestones and current position.
Funding Milestone Targets Are milestone targets being met? — — —
Funding Milestone Targets Funding Milestone Targets Section Score / Rule Check — — If D82 = Yes and E83 <= 3, the Budget Health shown in the summary and overall weighted score is capped at 3 (Amber max).
AI Usage & Maturity Where is AI being used? Early but expect Delivery, development, testing 4 —
AI Usage & Maturity % of team actively using AI 0.9 4 —
AI Usage & Maturity AI maturity level Experimental to embedded 4 —
AI Usage & Maturity Observed impact from AI Still early 4 —
AI Usage & Maturity AI Usage & Maturity Section Score — — Auto-calculated
Tech Stack Used Current tech stack GCP, Azure, Jira, Confluence, ADO, Slack, Gemini, CloudM, Workspace — —
Tech Stack Used Material tech changes this period n/a — —
Tech Stack Used Tech risks or constraints n/a — —
Tech Stack Used Tech Stack Used Section Score — — Auto-calculated
New Opportunities Upsell / cross-sell opportunities N/A — —
New Opportunities Efficiency / delivery improvement opportunities N/A — —
New Opportunities Innovation or expansion opportunities N/A — —
New Opportunities New Opportunities Section Score — — Auto-calculated
Lombard - GitHub 80 rows
Section Question Response Score Notes
Context Client / Customer Account Lombard — —
Context Primary Focus for This Period GitHub assessment and pipleine creation — —
Context Key Wins / Achievements Mark making good progress on assessment portion. Has captured timelines and milestones. — —
Context Biggest Challenges / Blockers Not currently — —
Context Major Changes (scope/team/budget/customer) N/A — —
Context Funding / Commercial Changes N/A — —
Context Single Most Important Leadership / Account Escalation N/A — —
Budget Health Budget type T&M — Budget tracking well for April
Budget Health Total budget vs utilised budget On budget 5 —
Budget Health % of timeline elapsed vs % of budget consumed Aligns 5 —
Budget Health Is spend aligned with progress? Yes 5 —
Budget Health Forecasted overrun or underrun risk Currently on budget 5 —
Budget Health Cost inefficiencies or rework impacting budget n/a 5 —
Budget Health Confidence in budget landing High 5 —
Budget Health Budget Health Section Score — 5 Auto-calculated
Timeline Health Key milestones on track On track 4 —
Timeline Health % of planned work completed vs expected On track 4 —
Timeline Health Schedule slippage this period On track 4 —
Timeline Health Dependencies impacting timelines N/A 4 —
Timeline Health Confidence in delivery dates Overall high 4 —
Timeline Health Timeline Health Section Score — 4 Auto-calculated
Delivery Health Consistency of delivery this period On track 5 —
Delivery Health Velocity trend On track 5 —
Delivery Health Customer value delivered On track to do so 5 —
Delivery Health Flow bottlenecks or blockers N/A 4 —
Delivery Health % of committed vs delivered work On track 4 —
Delivery Health Delivery Health Section Score — 4.60 Auto-calculated
Scope Health Scope clarity Yes 5 —
Scope Health Scope stability No 5 —
Scope Health Change control effectiveness Yes when applicable 5 —
Scope Health Impact of scope changes on budget/timeline To be managed if applicable 5 —
Scope Health Backlog health Refined 5 —
Scope Health Scope Health Section Score — 5 Auto-calculated
Quality Engineering quality Team quality is high 5 —
Quality Compliance / data privacy / security In line with Lombards security standards 5 —
Quality Defect and rework trend — 5 —
Quality Test coverage and automation maturity — 5 —
Quality Production incidents or escaped defects — 5 —
Quality Data classification and handling — 5 —
Quality Access controls and environment segregation Access controls run by Lombard Infra and SecOps 5 —
Quality Data storage and protection controls In line with Lombards security standards 5 —
Quality AI data usage controls In line with Lombards security standards 5 Teams utilize AI tools fequently. Lombard is an AI first org. Gemini tools mostly in play. Cursor discussions underway
Quality Breaches, near misses, or governance gaps Not applicable to Synthesis actions in April 5 —
Quality Compliance confidence High 5 —
Quality Quality Section Score — 5 Auto-calculated
Team Wellbeing Team morale High 5 —
Team Wellbeing Workload sustainability Workload currently sustainable - constant monitoring 5 —
Team Wellbeing Burnout risk Monitoring 4 —
Team Wellbeing Team stability Stable 5 —
Team Wellbeing Psychological safety and collaboration Regular team meetings and check ins in play. Customer is extremely considerate to team needs as well 5 —
Team Wellbeing Team Wellbeing Section Score — 5 Auto-calculated
Customer Satisfaction & Engagement Customer sentiment Positive 5 —
Customer Satisfaction & Engagement Customer engagement level Regular, fair and transaprent. Inclusive communication consistently 5 —
Customer Satisfaction & Engagement Feedback received this period Consistent positive feedback towards our team. Call outs noted and communicated 5 —
Customer Satisfaction & Engagement Responsiveness and collaboration Overall high engagement 5 —
Customer Satisfaction & Engagement Escalations or relationship strain Not currently 5 —
Customer Satisfaction & Engagement Customer Satisfaction & Engagement Section Score — 5 Auto-calculated
Risk & Issue Management Active risks visibility Early identification regularly - tracked and managed 5 —
Risk & Issue Management Active issues visibility All issues are owned 5 —
Risk & Issue Management Mitigation effectiveness Yes 5 —
Risk & Issue Management Critical risks or issues No critical risks currently 5 —
Risk & Issue Management Issue resolution responsiveness To date, issues handled quickly 5 —
Risk & Issue Management Emerging risks / early warning signals Mostly early idenfication 5 Risks are noted early and managed
Risk & Issue Management Risk & Issue Management Section Score — 5 Auto-calculated
Funding Milestone Targets Funding milestones in scope? n/a — If applicable, describe milestones and current position.
Funding Milestone Targets Are milestone targets being met? n/a — —
Funding Milestone Targets Funding Milestone Targets Section Score / Rule Check — — If D82 = Yes and E83 <= 3, the Budget Health shown in the summary and overall weighted score is capped at 3 (Amber max).
AI Usage & Maturity Where is AI being used? Delivery, development, testing 4 —
AI Usage & Maturity % of team actively using AI 0.9 4 —
AI Usage & Maturity AI maturity level Experimental to embedded 4 —
AI Usage & Maturity Observed impact from AI Speed of delivery, daily efficiencies, operational efficiencies 4 —
AI Usage & Maturity AI Usage & Maturity Section Score — — Auto-calculated
Tech Stack Used Current tech stack GCP, Azure, Jira, Confluence, ADO, Slack, Gemini — —
Tech Stack Used Material tech changes this period n/a — —
Tech Stack Used Tech risks or constraints n/a — —
Tech Stack Used Tech Stack Used Section Score — — Auto-calculated
New Opportunities Upsell / cross-sell opportunities N/A — —
New Opportunities Efficiency / delivery improvement opportunities N/A — —
New Opportunities Innovation or expansion opportunities N/A — —
New Opportunities New Opportunities Section Score — — Auto-calculated
Lombard - Strategic Contract 167 rows
Section Question Response Score Notes
Context Client / Customer Account Lombard — —
Context Primary Focus for This Period Migration/ Infra Team objectives, Data Team objectives, Partner Team objectives, FinOps optimization — —
Context Key Wins / Achievements Marion upsell to 100%, Data Team(Duncan and Wian) - receiving positive feedback on Datawarehouse and IFRS17 progress, Infra Team - Ivan focussed role is receiving positive feedback from Schalk, Leandre and Rui efforts on Azure, as well as Leandre assistance to Partner Team going well. — —
Context Biggest Challenges / Blockers No blockers. Potential challenge being monitored is Rui and Leandre time for the next month - steps are in place to try and keep their time at the 40% split. If the requirements are for more time, this will be addressed and reassessed. Emily and Schalk are aware. — —
Context Major Changes (scope/team/budget/customer) Marion upsell to 100% is a change to the team and budget — —
Context Funding / Commercial Changes Commercial adjustment for Marions additional 50%. Deals have been won. Separate reporting for these - CloudM and Github engagements — —
Context Single Most Important Leadership / Account Escalation Monitoring of Task Force allocation is currently the most important call out - in hand — —
Budget Health Budget type Fixed price — —
Budget Health Total budget vs utilised budget On budget 4 Adjusted to a 4 given minor discrepancy to planned budget due to resource limitations at the time
Budget Health % of timeline elapsed vs % of budget consumed Aligns 5 —
Budget Health Is spend aligned with progress? Yes 5 —
Budget Health Forecasted overrun or underrun risk Currently on budget 5 —
Budget Health Cost inefficiencies or rework impacting budget n/a 5 —
Budget Health Confidence in budget landing High 5 —
Budget Health Budget Health Section Score — 4.75 Auto-calculated
Timeline Health Key milestones on track On track 5 —
Timeline Health % of planned work completed vs expected On track 5 —
Timeline Health Schedule slippage this period On track 5 —
Timeline Health Dependencies impacting timelines Odek dependency affecting speed of server migrations from Terraco 3 Vendor dependency. Synthesis not at risk but score is a 3 due to their delay
Timeline Health Confidence in delivery dates Overall high 4 —
Timeline Health Timeline Health Section Score — 4.25 Auto-calculated
Delivery Health Consistency of delivery this period On track 5 —
Delivery Health Velocity trend Oor value flow (value delivered for the customer) On track where applicable, 5 shape up in use as a framework - velocity not a measurement
Delivery Health Customer value delivered On track 5 —
Delivery Health Flow bottlenecks or blockers Challenges in ensuring all requirements are captured correctly - Partner Squad 3 Challenges in ensuring all requirements are captured correctly in Shaping is still being refined continuously - business responsible for this. Scored a 3 as is ongoing and is being addressed
Delivery Health % of committed vs delivered work On track 4 —
Delivery Health Delivery Health Section Score — 4.40 Auto-calculated
Scope Health Scope clarity Contract - fixed billing no scope. Team on track with what Lombard is asking of us. 5 —
Scope Health Scope stability Contract - fixed billing no scope. Team on track with what Lombard is asking of us. 5 —
Scope Health Change control effectiveness Contract - fixed billing no scope. Team on track with what Lombard is asking of us. 5 —
Scope Health Impact of scope changes on budget/timeline Contract - fixed billing no scope. Team on track with what Lombard is asking of us. 5 —
Scope Health Backlog health Contract - fixed billing no scope. Team on track with what Lombard is asking of us. 5 —
Scope Health Scope Health Section Score — — Auto-calculated
Quality Engineering quality Team quality is high 5 —
Quality Compliance / data privacy / security In line with Lombards security standards 5 —
Quality Defect and rework trend — 5 —
Quality Test coverage and automation maturity — 5 —
Quality Production incidents or escaped defects — 5 —
Quality Data classification and handling — 5 —
Quality Access controls and environment segregation Access controls run by Lombard Infra and SecOps 5 —
Quality Data storage and protection controls In line with Lombards security standards 5 —
Quality AI data usage controls In line with Lombards security standards 5 Teams utilize AI tools fequently. Lombard is an AI first org. Gemini tools mostly in play. Cursor discussions underway
Quality Breaches, near misses, or governance gaps Not applicable to Synthesis actions in April 5 —
Quality Compliance confidence High 5 —
Quality Quality Section Score — 5 Auto-calculated
Team Wellbeing Team morale High 5 —
Team Wellbeing Workload sustainability Workload currently sustainable - contant monitoring 5 —
Team Wellbeing Burnout risk Monitoring 4 —
Team Wellbeing Team stability Stable 5 —
Team Wellbeing Psychological safety and collaboration Regular team meetings and check ins in play. Customer is extremely considerate to team needs as well 5 —
Team Wellbeing Team Wellbeing Section Score — 4.80 Auto-calculated
Customer Satisfaction & Engagement Customer sentiment Positive 5 —
Customer Satisfaction & Engagement Customer engagement level Regular, fair and transaprent. Inclusive communication consistently 5 —
Customer Satisfaction & Engagement Feedback received this period Consistent positive feedback towards our team. Call outs noted and communicated 5 —
Customer Satisfaction & Engagement Responsiveness and collaboration Overall high engagement 5 —
Customer Satisfaction & Engagement Escalations or relationship strain Not currently 5 —
Customer Satisfaction & Engagement Customer Satisfaction & Engagement Section Score — 5 Auto-calculated
Risk & Issue Management Active risks visibility Early identification regularly - tracked and managed 5 —
Risk & Issue Management Active issues visibility All issues are owned 5 —
Risk & Issue Management Mitigation effectiveness Yes 5 —
Risk & Issue Management Critical risks or issues No critical risks currently 5 —
Risk & Issue Management Issue resolution responsiveness To date, issues handled quickly 5 —
Risk & Issue Management Emerging risks / early warning signals Mostly early idenfication 5 —
Risk & Issue Management Risk & Issue Management Section Score — 5 Auto-calculated
Funding Milestone Targets Funding milestones in scope? No — If applicable, describe milestones and current position.
Funding Milestone Targets Are milestone targets being met? — — —
Funding Milestone Targets Funding Milestone Targets Section Score / Rule Check — — If D82 = Yes and E83 <= 3, the Budget Health shown in the summary and overall weighted score is capped at 3 (Amber max).
AI Usage & Maturity Where is AI being used? Delivery, PM, BA, development, testing — —
AI Usage & Maturity % of team actively using AI 0.9 — —
AI Usage & Maturity AI maturity level Experimental to embedded — —
AI Usage & Maturity Observed impact from AI Speed of delivery, daily efficiencies, operational efficiencies — —
AI Usage & Maturity How are the team using AI on the project? — — —
AI Usage & Maturity AI Usage & Maturity Section Score — — Auto-calculated
Tech Stack Used Current tech stack GCP, Azure, Jira, Confluence, ADO, Slack, Gemini — —
Tech Stack Used Material tech changes this period n/a — —
Tech Stack Used Tech risks or constraints n/a — —
Tech Stack Used Tech Stack Used Section Score — — Auto-calculated
Project Compliance Does the project handle senstive data? — — —
Project Compliance If yes, what kind of data is being processed? — — —
Project Compliance If this data is stored on Synthesis Infrastructure or resources, has it been approved by the BU Head and Shared Services Executive? — — —

Showing first 80 of 167 commentary rows for this project.

May 2026_Team Augmentation_Change Request AV003_Comprehensive Internal Project Health Report 80 rows
Section Question Response Score Notes
Context Client / Customer Account Capture the client or account name for account-level risk reporting. — —
Context Primary Focus for This Period Complete if there was a goal for this month — —
Context Key Wins / Achievements
  • Looking to increase the role, aligned Synthesis growth plan with Yoni (CEO). Likely new contract in July/August.-Currently have a busy data lead, Zander has taken on more ownership on the sessions. - Working towards data goals and a list of achievements. -Zander is getting exposure to new domains (data governance, Snowflake, agentic solutions)
  • Customer is incredibly understanding and he gets to work directly with the CEO (Yoni). Learning to manage customer expectations and prioritise with them. It is clear that he is thinking logically through all of these delivery practices.
  • Zander is comfortable with the accountability he has had to take on and is comfortable working in an augmentation engagement.
— —
Context Biggest Challenges / Blockers — — —
Context Major Changes (scope/team/budget/customer) — — —
Context Funding / Commercial Changes — — —
Context Single Most Important Leadership / Account Escalation — — —
Budget Health Budget type Time & Materials Engagement — —
Budget Health Total budget vs utilised budget — 4 —
Budget Health % of timeline elapsed vs % of budget consumed — 4 —
Budget Health Is spend aligned with progress? — 4 —
Budget Health Forecasted overrun or underrun risk — 5 Budget may be recovered based on actual number of days per month versus the 21 days commercials are typcially based on. Public holidays may impact further and should be monitored.
Budget Health Cost inefficiencies or rework impacting budget None to be reported. 5 —
Budget Health Confidence in budget landing Medium 5 Uncertainty regarding leave, actual working days etc. may result in minor under utilisation (accepted risk)
Budget Health Budget Health Section Score — 4.50 Auto-calculated
Timeline Health Key milestones on track Timelines will need to be adjutsed on their current plan 4 —
Timeline Health % of planned work completed vs expected For the grant they have 4 ML points, 1 Snowflake integration. These will truly start when given the grant - currently on "Side quests" . 4 —
Timeline Health Schedule slippage this period The majority of the work I’m currently handling is structured as rolling milestones, primarily due to the size and complexity of the tasks. Much of the work is delivered as vertical slices, meaning each piece needs to be fully completed before it can be properly tested and validated. Where there have been delays, they have generally been driven by external dependencies or shifts in direction from upper management. In some cases, this has introduced additional scope, which has impacted timelines. 3.50 —
Timeline Health Dependencies impacting timelines Internal 4 —
Timeline Health Confidence in delivery dates Very experimental, cant drill down exact dates - the client is fully understanding of this. 3 —
Timeline Health Timeline Health Section Score — 3.50 Auto-calculated
Delivery Health Consistency of delivery this period In my check-ins with the team lead and Joni, I’ve consistently received positive feedback on my velocity and delivery. 4 —
Delivery Health Velocity trend My velocity is generally stable, and I’d consider it high. However, dependencies on others can sometimes impact delivery timelines. This is well understood by both my team lead and the CEO. The positive side is that the foundational work I’m doing now will improve my velocity over time, especially once we move into a more in-depth implementation phase. 4 —
Delivery Health Customer value delivered Yes, some of my work has already been used to help assess the current state of annual financial planning, as well as the underlying statistics and assumptions. The risk model is still a bit unstable at this stage due to a misalignment around the fields used, but that’s being addressed. I’m also confident that the data governance framework I’m working on will bring significant benefits once implemented. 4 —
Delivery Health Flow bottlenecks or blockers Currently struggling with an American in charge of CRM - defensive and hard to work with (Other teams feel the same). Using communication and other methods to continue but may become a bigger issue in the future. 3 —
Delivery Health % of committed vs delivered work It’s difficult to assign an exact percentage of work completed, as there are multiple streams running in parallel, which isn’t always ideal. However, everything I’ve worked on is actively progressing and being brought to completion. Priorities can sometimes delay immediate progress on certain tasks, but they do get completed over time. Overall, I’d estimate around 80% completion with 100% commitment. 4 —
Delivery Health Delivery Health Section Score — 3.80 Auto-calculated
Scope Health Scope clarity Yes, there was some initial uncertainty around the scope at the start of the project. However, I aligned with the team lead, and we now have bi-weekly check-ins to ensure everything stays on track. I also have weekly sessions with the CEO to keep everyone aligned and on the same page. 5 —
Scope Health Scope stability Improved since April - a lot of alignment sessions. Most of my work at Avenews is grant-related, but there has been some scope creep with additional responsibilities like data governance implementation, the risk model, and the liquidity model. This has been manageable so far since I haven’t fully started on the grant tasks yet. However, it could become a challenge once I’m actively working on grant deliverables alongside these additional responsibilities. 4 —
Scope Health Change control effectiveness Yes, when changes are needed to a model or query, we usually align on a common approach and way of working. If there’s a difference in opinion, I make my perspective clear and explain the reasoning behind it. 4 —
Scope Health Impact of scope changes on budget/timeline This has been an ongoing challenge at Avenews. I’ve discussed it with my team lead and have made it a habit to raise any timeline-related risks as early as possible. The positive side is that both the team lead and CEO understand that a startup environment is highly dynamic, so some level of uncertainty comes with it. Another factor is that grant-related tasks don’t always have clearly defined timelines, which can impact how much work can be completed before timelines are finalised. 4 —
Scope Health Backlog health Avenews does run sprints, but the process isn’t always consistently managed, and transitions between sprints can sometimes feel unstructured. The team lead is aware of this and is actively working on standardising the process. From my side, I have full visibility of my workload and keep the board clean and up to date. As the only ML Engineer on the team, I’m responsible for creating and managing my own tickets. 4 —
Scope Health Scope Health Section Score — — Auto-calculated
Quality Engineering quality There are test scripts in the repository for the endpoints, along with API documentation, and Avenews also has a dedicated tester. Since I mainly work on the back-end, I make sure tests are in place and validate queries in the CRM together with the subject matter expert. 4 —
Quality Compliance / data privacy / security They hired a compliance officer. Company is a 3, Zanders work is higher and intacct. As mentioned previously, this is still a work in progress and will be addressed as part of the data governance implementation. So far, everything has been running smoothly, and as the business continues to mature quickly, we’ll be introducing stronger security and compliance measures. 3 —
Quality Defect and rework trend Reworking the model logic itself hasn’t really been an issue. Most changes only come in when Joni requests additional data points or refinements. The bulk of the rework actually happens during retraining, which can take quite a long time, but that’s not due to problems with the code, rather it’s about improving and adapting to the underlying data at Avenews. 4 —
Quality Test coverage and automation maturity Unit tests etc very good. Retraining is a manual process - plans to move the local models into AWS> 3 —
Quality Production incidents or escaped defects The risk model currently in production experienced some issues, not due to code, but rather misalignment around the variables used during testing. I make it a priority to ensure everything is properly validated and functioning as expected in production. 4 —
Quality Data classification and handling We’ve had Avenews-specific training on data policies and protections, and I apply those principles in all my work. I avoid pulling or processing sensitive data, and if it is present, I ensure it cannot be linked back to any individual or business. 4 —
Quality Access controls and environment segregation I currently have access to most of the data, as I work across the full data landscape within Avenews. There are separate environments for development, testing, and production, and I primarily work with production data. Access to modify data, such as editing tables, is controlled through least-privilege principles to limit unnecessary changes. 4 —
Quality Data storage and protection controls The team’s data is hosted on Amazon Web Services, where it is encrypted by default. In addition, all access to these data sources is secured through authentication controls. 5 —
Quality AI data usage controls I don’t upload any sensitive data during AI sessions, those are only used for troubleshooting and idea generation. At the moment, the data in our database isn’t masked, which does pose a risk. However, I’m working closely with the compliance officer, and we expect this to be addressed as part of the upcoming data governance framework. 4 —
Quality Breaches, near misses, or governance gaps This is an interesting area. Nothing like this has occurred since I joined the project, but there are clear guidelines and reporting processes in place should anything go wrong. It also highlights why we’re investing in data governance. 5 —
Quality Compliance confidence Avenews recently brought on a Compliance Officer, and I’ve been working closely with him on complaince and how that fits into data governance. He walked me through how we collect and use data, which gave me a much clearer understanding of the process. I have also focused on avoiding the use of sensitive data during model development and deployment. 4 —
Quality Quality Section Score — 4 Auto-calculated
Team Wellbeing Team morale My overall satisfaction is high. I’m happy where I am and value the growth and learning I’m gaining. 5 —
Team Wellbeing Workload sustainability The pace fluctuates quite a bit, when something urgent comes up, I shift focus to that. Outside of those moments, I’ve learned to manage my time effectively and stay on track. If any concerns around timelines arise, I’m comfortable discussing them with my team lead, who is supportive and helpful. 4 —
Team Wellbeing Burnout risk There are likely some signs of burnout, given the constant flow of tasks and ideas on the project. That said, I’ve adapted to the pace and have been able to stay on top of the work and continue delivering. 4 —
Team Wellbeing Team stability Still hugely the key man dependency. Role movement still in talks. 3 —
Team Wellbeing Psychological safety and collaboration Yes, I have a very good relatiosnhip with the team lead and their CEO. These conversations have been happening and it has been well received 5 —
Team Wellbeing Team Wellbeing Section Score — 4.20 Auto-calculated
Customer Satisfaction & Engagement Customer sentiment Positive 5 Zander has set up bi-weekly check-ins with their lead to ensure alignment.
Customer Satisfaction & Engagement Customer engagement level — 4 —
Customer Satisfaction & Engagement Feedback received this period — 5 —
Customer Satisfaction & Engagement Responsiveness and collaboration Hugely improved, internally & at client. To get the work done you need to collaborate hugely. 4 —
Customer Satisfaction & Engagement Escalations or relationship strain — 4 —
Customer Satisfaction & Engagement Customer Satisfaction & Engagement Section Score — 4.40 Auto-calculated
Risk & Issue Management Active risks visibility Customer is responsible for risk management, our team make them aware of the risks or issues. No formal management in place within the customers environment. 1 —
Risk & Issue Management Active issues visibility Raised via informal channels such as word or mouth. 2 —
Risk & Issue Management Mitigation effectiveness Mitigations not always followed through. Slowly improving 2 —
Risk & Issue Management Critical risks or issues — 3 —
Risk & Issue Management Issue resolution responsiveness — 3 —
Risk & Issue Management Emerging risks / early warning signals Start up with typically react maturity levels, not good for high-impact projects 1 —
Risk & Issue Management Risk & Issue Management Section Score — 2 Auto-calculated
Funding Milestone Targets Funding milestones in scope? No — If applicable, describe milestones and current position.
Funding Milestone Targets Are milestone targets being met? — — —
Funding Milestone Targets Funding Milestone Targets Section Score / Rule Check — — If D82 = Yes and E83 <= 3, the Budget Health shown in the summary and overall weighted score is capped at 3 (Amber max).
AI Usage & Maturity Where is AI being used? Still the same. Research, Upskilling and Personal Enablement to compliment delivery. Debugging in unfamiliar domains. — —
AI Usage & Maturity % of team actively using AI 1 — —
AI Usage & Maturity AI maturity level Experimental — —
AI Usage & Maturity Observed impact from AI Productivity, Speed — —
AI Usage & Maturity AI Usage & Maturity Section Score — — Auto-calculated
Tech Stack Used Current tech stack React (historically), MongoDB, Zoho Analytics, Python, Fast API, Swagger, Jupiter Notebooks, AWS — —
Tech Stack Used Material tech changes this period NA — —
Tech Stack Used Tech risks or constraints NA — —
Tech Stack Used Tech Stack Used Section Score — — Auto-calculated
New Opportunities Upsell / cross-sell opportunities Allocation role level increase. — —
New Opportunities Efficiency / delivery improvement opportunities — — —
New Opportunities Innovation or expansion opportunities — — —
New Opportunities New Opportunities Section Score — — Auto-calculated
Principal Solutions Architect 89 rows
Section Question Response Score Notes
Context Client / Customer Account Capture the client or account name for account-level risk reporting. — —
Context Primary Focus for This Period — — —
Context Key Wins / Achievements — — —
Context Biggest Challenges / Blockers — — —
Context Major Changes (scope/team/budget/customer) — — —
Context Funding / Commercial Changes — — —
Context Single Most Important Leadership / Account Escalation — — —
Budget Health Budget type — — —
Budget Health Total budget vs utilised budget — 2 —
Budget Health % of timeline elapsed vs % of budget consumed — 4 —
Budget Health Is spend aligned with progress? — 3 —
Budget Health Forecasted overrun or underrun risk — 1 —
Budget Health Cost inefficiencies or rework impacting budget — 5 —
Budget Health Confidence in budget landing — 5 —
Budget Health Budget Health Section Score — 3.25 Auto-calculated
Timeline Health Key milestones on track — — —
Timeline Health % of planned work completed vs expected — — —
Timeline Health Schedule slippage this period — — —
Timeline Health Dependencies impacting timelines — — —
Timeline Health Confidence in delivery dates — — —
Timeline Health Timeline Health Section Score — — Auto-calculated
Delivery Health Consistency of delivery this period — — —
Delivery Health Velocity trend Oor value flow (value delivered for the customer) — — —
Delivery Health Customer value delivered — — —
Delivery Health Flow bottlenecks or blockers — — —
Delivery Health % of committed vs delivered work — — —
Delivery Health Delivery Health Section Score — — Auto-calculated
Scope Health Scope clarity — — —
Scope Health Scope stability — — —
Scope Health Change control effectiveness — — —
Scope Health Impact of scope changes on budget/timeline — — —
Scope Health Backlog health — — —
Scope Health Scope Health Section Score — — Auto-calculated
Quality Engineering quality — — —
Quality Compliance / data privacy / security — — Add details here
Quality Defect and rework trend — — —
Quality Test coverage and automation maturity — — —
Quality Production incidents or escaped defects — — —
Quality Data classification and handling — 5 Add details here
Quality Access controls and environment segregation — — —
Quality Data storage and protection controls — — —
Quality AI data usage controls — — —
Quality Breaches, near misses, or governance gaps — — —
Quality Compliance confidence — — —
Quality Quality Section Score — 5 Auto-calculated
Team Wellbeing Team morale — — —
Team Wellbeing Workload sustainability — — —
Team Wellbeing Burnout risk — — —
Team Wellbeing Team stability — — —
Team Wellbeing Psychological safety and collaboration — — —
Team Wellbeing Team Wellbeing Section Score — — Auto-calculated
Customer Satisfaction & Engagement Customer sentiment — — —
Customer Satisfaction & Engagement Customer engagement level — — —
Customer Satisfaction & Engagement Feedback received this period — — —
Customer Satisfaction & Engagement Responsiveness and collaboration — — —
Customer Satisfaction & Engagement Escalations or relationship strain — — —
Customer Satisfaction & Engagement Customer Satisfaction & Engagement Section Score — — Auto-calculated
Risk & Issue Management Active risks visibility — — —
Risk & Issue Management Active issues visibility — — —
Risk & Issue Management Mitigation effectiveness — — —
Risk & Issue Management Critical risks or issues — — —
Risk & Issue Management Issue resolution responsiveness — — —
Risk & Issue Management Emerging risks / early warning signals — — —
Risk & Issue Management Risk & Issue Management Section Score — — Auto-calculated
Funding Milestone Targets Funding milestones in scope? Yes — If applicable, describe milestones and current position.
Funding Milestone Targets Are milestone targets being met? — 4 —
Funding Milestone Targets Funding Milestone Targets Section Score / Rule Check — 4 If D82 = Yes and E83 <= 3, the Budget Health shown in the summary and overall weighted score is capped at 3 (Amber max).
AI Usage & Maturity Confirm with the customer if the use of AI is permitted for the project — — —
AI Usage & Maturity Where is AI being used? — — —
AI Usage & Maturity % of team actively using AI — — —
AI Usage & Maturity AI maturity level — — —
AI Usage & Maturity Observed impact from AI — — —
AI Usage & Maturity How are the team using AI on the project? — — —
AI Usage & Maturity AI Usage & Maturity Section Score — — Auto-calculated
Tech Stack Used Current tech stack — — —
Tech Stack Used Material tech changes this period — — —
Tech Stack Used Tech risks or constraints — — —
Tech Stack Used Tech Stack Used Section Score — — Auto-calculated
Project Compliance Does the project handle senstive data? — — —
Project Compliance What regulations must be complied with — — —

Showing first 80 of 89 commentary rows for this project.

SBG - CrediAssist POC (Partially AWS Funded) 89 rows
Section Question Response Score Notes
Context Client / Customer Account Standard Bank — —
Context Primary Focus for This Period
  • Discovery of the as is process and data inputs to ensure we understand the requirements before beginning with the build phase
  • Technical architecture alignment
  • Produce a working solution by the end of June to prove initial value
— —
Context Key Wins / Achievements — — —
Context Biggest Challenges / Blockers
  • Access delays means that we do not have access to their environment and tooling to begin with the POC in a practical sense.
  • Stakeholder availability from the client side was reduced during week 2 of the engagement, which meant feedback loops were not as effective.
    		• — —
    Context Major Changes (scope/team/budget/customer) — — —
    Context Funding / Commercial Changes Funding has been approved by AWS but the dates need to be amended to reflect the actual project timelines. PO is still outstanding from Standard Bank which means that we cannot begin with invoicing. — —
    Context Single Most Important Leadership / Account Escalation — — —
    Budget Health Budget type Fixed Billing, Fixed Allocation of team members. Synthesis investment + AWS funding — —
    Budget Health Total budget vs utilised budget — 5 —
    Budget Health % of timeline elapsed vs % of budget consumed — 5 —
    Budget Health Is spend aligned with progress? — 5 —
    Budget Health Forecasted overrun or underrun risk — 5 —
    Budget Health Cost inefficiencies or rework impacting budget — 5 —
    Budget Health Confidence in budget landing — 5 —
    Budget Health Budget Health Section Score — 5 Auto-calculated
    Timeline Health Key milestones on track Access delays have caused some initial disruption. 3 They would like to see value by the end of June, should access delays persist this timeline may not be feasible.
    Timeline Health % of planned work completed vs expected — — —
    Timeline Health Schedule slippage this period — — —
    Timeline Health Dependencies impacting timelines — — —
    Timeline Health Confidence in delivery dates — — —
    Timeline Health Timeline Health Section Score — 3 Auto-calculated
    Delivery Health Consistency of delivery this period — — —
    Delivery Health Velocity trend Oor value flow (value delivered for the customer) — — —
    Delivery Health Customer value delivered — — —
    Delivery Health Flow bottlenecks or blockers — — —
    Delivery Health % of committed vs delivered work — — —
    Delivery Health Delivery Health Section Score — — Auto-calculated
    Scope Health Scope clarity — — —
    Scope Health Scope stability — — —
    Scope Health Change control effectiveness — — —
    Scope Health Impact of scope changes on budget/timeline — — —
    Scope Health Backlog health — — —
    Scope Health Scope Health Section Score — — Auto-calculated
    Quality Engineering quality — — —
    Quality Compliance / data privacy / security — — Add details here
    Quality Defect and rework trend — — —
    Quality Test coverage and automation maturity — — —
    Quality Production incidents or escaped defects — — —
    Quality Data classification and handling — 5 Add details here
    Quality Access controls and environment segregation — — —
    Quality Data storage and protection controls — — —
    Quality AI data usage controls — — —
    Quality Breaches, near misses, or governance gaps — — —
    Quality Compliance confidence — — —
    Quality Quality Section Score — 5 Auto-calculated
    Team Wellbeing Team morale — 3 This is a team with many different personalities, so we have not seen them form strong bondds as yet. Not very engaged during client workshops.
    Team Wellbeing Workload sustainability — 5 —
    Team Wellbeing Burnout risk — 5 —
    Team Wellbeing Team stability — 5 —
    Team Wellbeing Psychological safety and collaboration — 3 Very introverted individuals and some team members will need to step outside their comfort zone to work with new technology stacks for this project, initial sense of discomfort but just needs monitoring for now.
    Team Wellbeing Team Wellbeing Section Score — 4.20 Auto-calculated
    Customer Satisfaction & Engagement Customer sentiment — — —
    Customer Satisfaction & Engagement Customer engagement level — — —
    Customer Satisfaction & Engagement Feedback received this period — — —
    Customer Satisfaction & Engagement Responsiveness and collaboration — — —
    Customer Satisfaction & Engagement Escalations or relationship strain — — —
    Customer Satisfaction & Engagement Customer Satisfaction & Engagement Section Score — — Auto-calculated
    Risk & Issue Management Active risks visibility — 5 —
    Risk & Issue Management Active issues visibility — 3 —
    Risk & Issue Management Mitigation effectiveness — 3 —
    Risk & Issue Management Critical risks or issues — 3 —
    Risk & Issue Management Issue resolution responsiveness — 3 —
    Risk & Issue Management Emerging risks / early warning signals — 5 —
    Risk & Issue Management Risk & Issue Management Section Score — 3.67 Auto-calculated
    Funding Milestone Targets Funding milestones in scope? Yes — POC sign off would be required before the project end date on the AWS portal. Date to be revised to align with project duration to avoid any key risks.
    Funding Milestone Targets Are milestone targets being met? — 5 —
    Funding Milestone Targets Funding Milestone Targets Section Score / Rule Check — 5 If D82 = Yes and E83 <= 3, the Budget Health shown in the summary and overall weighted score is capped at 3 (Amber max).
    AI Usage & Maturity Confirm with the customer if the use of AI is permitted for the project — — —
    AI Usage & Maturity Where is AI being used? — — —
    AI Usage & Maturity % of team actively using AI — — —
    AI Usage & Maturity AI maturity level — — —
    AI Usage & Maturity Observed impact from AI — — —
    AI Usage & Maturity How are the team using AI on the project? — — —
    AI Usage & Maturity AI Usage & Maturity Section Score — — Auto-calculated
    Tech Stack Used Current tech stack — — —
    Tech Stack Used Material tech changes this period — — —
    Tech Stack Used Tech risks or constraints — — —
    Tech Stack Used Tech Stack Used Section Score — — Auto-calculated
    Project Compliance Does the project handle senstive data? Yes, we are anticipating the ingestion of sensitive data for this project — The exact extent must still be determined as we are anonymising documents for the initial discovery, but the solution will need to process business documents like financial statements etc.
    Project Compliance What regulations must be complied with POPIA — —

    Showing first 80 of 89 commentary rows for this project.

    Vivo - VEOne Feature Team Extension - Jan - Dec 178 rows
    Section Question Response Score Notes
    Context Client / Customer Account Engen Petroleum (Pty) Limited — —
    Context Primary Focus for This Period

    We closed out our PI Q2 2026 and delivered all our planned items with. We did our final production release last week Thursday (25 Jun 2026). We also concluded PI planning for Q3 2026.

    For PI Q3 we mainly have Wetstock management that we want to complete along with 2 additional country rollouts.

    We also successfully completed the handover and rolloff of the Support team and Lead PM on the project.     		— —
    Context Key Wins / Achievements Completion of PI Q2 and multiple production releases to get all the PI Q2 work to production. — —
    Context Biggest Challenges / Blockers With the lead PM rolling off as well as the support team, there might be some growing pains for the team adjusting to the new structure having to juggle both feature work and support. The new internal PM will manage the priorities and will work closely with Jannes to try and balance responsibilities — —
    Context Major Changes (scope/team/budget/customer) Lead PM from Synthesis rolled off Support team rolled off — —
    Context Funding / Commercial Changes None — —
    Context Single Most Important Leadership / Account Escalation Support agreement coming to an end as well as Programme manager contract — —
    Budget Health Budget type Fixed price variable scope — —
    Budget Health Total budget vs utilised budget On budget 5 We are well within our planned spend and we are on track.
    Budget Health % of timeline elapsed vs % of budget consumed Aligns 5 —
    Budget Health Is spend aligned with progress? Yes 5 —
    Budget Health Forecasted overrun or underrun risk Currently on budget, maybe small risk of underrun 5 —
    Budget Health Cost inefficiencies or rework impacting budget N/A 5 —
    Budget Health Confidence in budget landing High 5 —
    Budget Health Budget Health Section Score — 5 Auto-calculated
    Timeline Health Key milestones on track On track 5 We have so far met all planned production deployments. Where features have been deprioritised or removed from release it has to do with other team dependancies not in our control. Vivo is managing these dependancies and well aware of delays not being on our side.
    Timeline Health % of planned work completed vs expected On track 4 We are mostly on track with one or two smaller features not on track because of delays in other teams. Vivo is well aware that these delays is not on our side and we can't do to much about it. It has been discussed and communicated well so everyone is on the same page.
    Timeline Health Schedule slippage this period On track 4 Even though we have some slight delays I believe we achieved all our main goals for this PI
    Timeline Health Dependencies impacting timelines On track 4 Dependencies impacting timeline are mostly other streams where they have limited resources having to serve multiple teams. So priority, availability and capacity is the issue there. Vivo is well aware of this risks and managing it actively.
    Timeline Health Confidence in delivery dates On track 5 Even though we had some slight delays I believe we achieved all our main goals for this PI
    Timeline Health Timeline Health Section Score — 4.40 Auto-calculated
    Delivery Health Consistency of delivery this period Excellent / no meaningful concern 5 Our team/stream have been delivering well and where there were delays it is because of dependencies on other streams
    Delivery Health Velocity trend Oor value flow (value delivered for the customer) Good but can be better if other teams start coming to the party 5 Velocity has been stable and even increasing slightly over the last few months. There might be some pressure now with the Support team rolling off and the feature team also picking up support items
    Delivery Health Customer value delivered High 5 —
    Delivery Health Flow bottlenecks or blockers Minimal blockers, manageble 4 Dependencies impacting timeline are mostly other streams where they have limited resources having to serve multiple teams. So priority, availability and capacity is the issue there. Vivo is well aware of this risks and managing it actively. The feature team having to take over suport as well might create some bottlenecks but only time will tell.
    Delivery Health % of committed vs delivered work On Track 4 Even though we had some slight delays we achieved all our main goals for this PI
    Delivery Health Delivery Health Section Score — 4.60 Auto-calculated
    Scope Health Scope clarity Clear 4 Scope clarity has been an issue earlier this year because of the issues experienced with the Vivo PM that was on the project and the lack of detailed requirements. This has been address and resolved and I feel with the new measures put in place as well as the new PM from Vivo being allocated this has improved. Even though we did not get clear requirements, our team went above and beyond to ask questions in order to get clarity on requirements which helped a lot in not falling behind to much.
    Scope Health Scope stability Stable 5 Because we do PI planning it is clear what we want to achieve within the PI. The major items have been stable with one or two smaller items flowing in as capacity opens up.
    Scope Health Change control effectiveness Needs a bit of work for Vivo internal teams 3 Change control was mostly managed by our team as we experienced some issues with the Vivo PM. All changes however was discussed with client and approved but I feel better record could have been kept from Vivo side.
    Scope Health Impact of scope changes on budget/timeline Minimal and it is managed by Vivo 5 Even though changes flowed in we always had discussion on items to be dropped to accommodate the changes so it is well in control.
    Scope Health Backlog health Good 5 We have a deep backlog and already have a high level view on items to plan for in the next PI
    Scope Health Scope Health Section Score — 4.40 Auto-calculated
    Quality Engineering quality Good 5 For us to deploy to production we have to go through CAB and just to get there we need, client approval, Technical approval and security approval. None of which would have been received of quality was down. We can also see that the amount if items coming through support is very low. About 90% of items coming through support are account setup related issues and very few are actual technical issues.
    Quality Compliance / data privacy / security Good 5 We are guided and reviewed by Vivo security team on any security related items
    Quality Defect and rework trend Good 5 The amount if items coming through support is very low. About 90% of items coming through support are account setup related issues and very few are actual technical issues.
    Quality Test coverage and automation maturity Good 4 This process is very mature in this team. If there is an area of growth it might be around automated testing. We can spend a bit more time here.
    Quality Production incidents or escaped defects Good 5 The amount if items coming through support is very low. About 90% of items coming through support are account setup related issues and very few are actual technical issues.
    Quality Data classification and handling Good 5 We are guided and reviewed by Vivo security team on any security related items
    Quality Access controls and environment segregation Good 5 We are guided and reviewed by Vivo security team on any security related items
    Quality Data storage and protection controls Good 5 We are guided and reviewed by Vivo security team on any security related items
    Quality AI data usage controls Good 5 —
    Quality Breaches, near misses, or governance gaps Good 5 No breaches or near misses aware off
    Quality Compliance confidence Good 5 —
    Quality Quality Section Score — 4.91 Auto-calculated
    Team Wellbeing Team morale High 5 We do team assessments every sprint and team health is looking good overall
    Team Wellbeing Workload sustainability Good 5 We do team assessments every sprint and team health is looking good overall
    Team Wellbeing Burnout risk Low 5 We do team assessments every sprint and team health is looking good overall
    Team Wellbeing Team stability Good 5 We do team assessments every sprint and team health is looking good overall
    Team Wellbeing Psychological safety and collaboration High 5 We do team assessments every sprint and team health is looking good overall
    Team Wellbeing Team Wellbeing Section Score — 5 Auto-calculated
    Customer Satisfaction & Engagement Customer sentiment Good 5 —
    Customer Satisfaction & Engagement Customer engagement level Good 5 —
    Customer Satisfaction & Engagement Feedback received this period Great 5 We received feedback during this month that the VE Team is one of the best delivery teams they have in Vivo. It is just a pitty that their cost pressure and our price is forcing them to reduce our headcount.
    Customer Satisfaction & Engagement Responsiveness and collaboration Good 5 We have an overall good relationship with the client and can have open and honest discussions with them. They are also open to give us feedback on where they stand and have been open about their cost challenges.
    Customer Satisfaction & Engagement Escalations or relationship strain Good 5 None at the moment
    Customer Satisfaction & Engagement Customer Satisfaction & Engagement Section Score — 5 Auto-calculated
    Risk & Issue Management Active risks visibility Needs some work from Vivo side 3 This sits with the client and even though we communicate all the risks with them I dont think they are doing to well in documenting it internally. They have now appointed a new internal PM to assist with this visibility across teams
    Risk & Issue Management Active issues visibility Needs some work from Vivo side 3 This sits with the client and even though we communicate all the risks with them I dont think they are doing to well in documenting it internally. They have now appointed a new internal PM to assist with this visibility across teams
    Risk & Issue Management Mitigation effectiveness Good 4 Most of the risks we raise are discussed and plans are put in place, but we or the internal VE One team can not always influence or implement mitigations as it is managed on higher level
    Risk & Issue Management Critical risks or issues Good 5 No critical risk currently exists
    Risk & Issue Management Issue resolution responsiveness Good 4 Most of the risks we raise are discussed and plans are put in place, but we or the internal VE One team can not always influence or implement mitigations as it is managed on higher level
    Risk & Issue Management Emerging risks / early warning signals Good 5 Issues and risks are raised early and discussed it is more the ability to implement mitigations where Vivo needs improvement. From a team point of view this is under control.
    Risk & Issue Management Risk & Issue Management Section Score — 4 Auto-calculated
    Funding Milestone Targets Funding milestones in scope? N/A — If applicable, describe milestones and current position.
    Funding Milestone Targets Are milestone targets being met? N/A — —
    Funding Milestone Targets Funding Milestone Targets Section Score / Rule Check — — If D82 = Yes and E83 <= 3, the Budget Health shown in the summary and overall weighted score is capped at 3 (Amber max).
    AI Usage & Maturity Confirm with the customer if the use of AI is permitted for the project No specific polisies — —
    AI Usage & Maturity Where is AI being used? Engineering and PM — —
    AI Usage & Maturity % of team actively using AI 1 — —
    AI Usage & Maturity AI maturity level Embedded — —
    AI Usage & Maturity Observed impact from AI Speed and Quality improvements — —
    AI Usage & Maturity How are the team using AI on the project? coding assistance — —
    AI Usage & Maturity AI Usage & Maturity Section Score — — Auto-calculated
    Tech Stack Used Current tech stack — — —
    Tech Stack Used Material tech changes this period — — —
    Tech Stack Used Tech risks or constraints — — —
    Tech Stack Used Tech Stack Used Section Score — — Auto-calculated
    Project Compliance Does the project handle senstive data? Yes — —
    Project Compliance What regulations must be complied with POPIA and GDPR — —

    Showing first 80 of 178 commentary rows for this project.

    Self-assessed AI maturity

    107
    Respondents
    45% of 236 employees
    2.28 / 4
    Avg self-level
    Weighted across L0–L4 responses
    30
    L3 + L4 respondents
    Strategic AI practitioners
    72
    L2 — Selective
    The “move the middle” cohort

    Distribution of self-reported AI maturity. L0 = resistant, L1 = experimental, L2 = selective, L3 = integrated, L4 = strategic.

    Level 0 — Resistant
    1 (1%)
    Level 1 — Experimental
    4 (4%)
    Level 2 — Selective
    72 (67%)
    Level 3 — Integrated
    24 (22%)
    Level 4 — Strategic
    6 (6%)

    Tools vs self-assessment — alignment matrix

    Cross-reference of self-assessed maturity against license allocation, scoped to the employees who responded. The Gap bucket is the most actionable — L3/L4 self-assessed individuals with zero tools provisioned.

    91
    ✅ Aligned
    L2+ self-assessed AND ≥1 tool licensed
    63
    ↑ Add methodology
    L2 with tools — ready to move to L3
    2
    ⚠ Critical gap
    L3/L4 self-assessed but no tools provisioned

    By department

    Department Responses Avg level Developers L0 L1 L2 L3 L4
    Code 24 2.08 0/24 1 0 19 4 0
    Cloud 16 2.25 0/16 0 1 11 3 1
    Regtech 14 2.43 0/14 0 0 9 4 1
    Intelligent Data 8 2.25 0/8 0 0 6 2 0
    Payment Centre of Excellence 8 2.5 0/8 0 0 5 2 1
    Managed Operations 6 2.17 0/6 0 1 4 0 1
    PMO 6 2.17 0/6 0 0 5 1 0
    Business Enablement & Operations 5 2.4 0/5 0 0 3 2 0
    Professional Services 5 2.6 0/5 0 0 2 3 0
    Sales 5 1.8 0/5 0 1 4 0 0
    Halo 3 2.0 0/3 0 0 3 0 0
    Technology 2 4.0 0/2 0 0 0 0 2
    (no department) 2 1.5 0/2 0 1 1 0 0
    Product Incubation 2 3.0 0/2 0 0 0 2 0
    Cryptography 1 3.0 0/1 0 0 0 1 0

    Strategic themes — next month

    Three concrete cohorts to action next month, derived from the data above. Numbers in bold are pulled from this snapshot; framings (PRIORITY / SCALE / AMPLIFY) mirror the ExCo report’s §4.

    🔴PRIORITY
    Tool provisioning gap
    • 2 respondents self-assessed L3/L4 with no tools provisioned.
    • 62 total employees have zero AI tools.
    • 3 unresolved license rows blocking accurate counts.
    Recommended actions
    • Provision Claude / ChatGPT licences for AI-active project teams this week.
    • Resolve outstanding unmatched handles via aliases.csv.
    • Establish a licence-request SLA (target: 48 hrs).
    🟡SCALE
    Move the L2 middle
    • 63 respondents at L2 with tools licensed.
    • They use AI reactively, not yet in their workflow.
    Recommended actions
    • Launch an L2→L3 upskilling cohort using internal AI methodology.
    • Focus on the largest BUs first — biggest leverage per session.
    • Pair each cohort member with a daily AI workflow exercise.
    🟢AMPLIFY
    L3 / L4 AI champions
    • 30 respondents self-assessed L3/L4 — strategic AI practitioners.
    • Chase non-respondents to fill in coverage gaps.
    Recommended actions
    • Formalise an internal AI champions programme.
    • Pair champions with lagging BUs as embedded mentors.
    • Capture champion playbooks for reuse across the org.

    Data quality

    3
    Unmatched license rows
    Tool emails not found in master — needs alias resolution
    1
    Tools awaiting export
    GitHub Copilot — not yet loaded for this snapshot
    107
    Assessment responses
    AI Maturity Self Assessment rows loaded
    7
    Unmatched maturity reports
    project_maturity rows not linked by project_key
    1
    Unmatched project people
    projects_people rows with blank employee_code

    Generated from data/2026-07/standardised/. Re-run python dashboard/build.py –month 2026-07 after refreshing data.