A fixed-scope 4-week AI Delivery System Audit for organisations already using Copilot, Cursor, Claude Code, Codex, or internal coding agents. See whether AI is reducing cycle time, or just increasing PR review load, governance risk, and spend.
Read the 2-page audit outline or validate the background on LinkedIn. No form, no signup to view the decision pack — opens instantly, print/PDF-ready.
Most AI coding programmes measure seats, usage, and code output. The expensive problems sit between code generation and production.
AI increases PR volume and surface area. Review queues grow, senior engineers carry hidden workload, and lead time stays flat.
Policy, secure usage, data handling, and code-quality gates are often less mature than the AI-assisted workflows already in use.
Large contexts, model mismatch, agent loops, and unowned experimentation quietly consume budget before anyone sees the quarterly bill.
Leadership is being asked harder questions before renewals, procurement reviews, customer due diligence, and board-level scrutiny.
A 4-week fixed-scope AI-SDLC diagnostic for engineering organisations already using AI coding tools. The output is a board- and engineering-ready decision pack, not a generic AI strategy deck, DevOps maturity assessment, or cloud transformation programme.
Two ways to get it. Pick whichever fits your process.
Want a tailored reply from Matt? Optional — submit your work email below. The outline opens instantly either way.
These are representative examples of the type of evidence the audit surfaces. Client-specific findings are redacted or validated under NDA.
Usage dashboards showed strong adoption. Flow analysis showed the bottleneck had moved to senior review capacity, with larger AI-assisted PRs increasing queue depth.
Decision supported: change review policy, PR sizing, and ownership before expanding licences.
Teams had guidance for AI-assisted coding, but delivery controls, dependency scanning, and reviewer expectations were inconsistent across repositories.
Decision supported: standardise AI-ready delivery controls before regulated customer due diligence.
Agent experiments, oversized context windows, and model mismatch made cost hard to attribute. Finance saw a bill; engineering lacked task-level accountability.
Decision supported: introduce routing, caching, and cost attribution before budget review.
Developers felt faster, but the evidence did not connect adoption to delivery outcomes. The audit reframed the renewal conversation around retained value.
Decision supported: continue, expand, constrain, or redesign the AI coding programme with evidence.
The audit is designed for busy engineering leaders. Most work is async, tool-agnostic, and based on existing delivery data.
Map tooling, DORA signals, PR flow, AI usage, team structure, and current governance posture.
Identify where AI adds velocity into constrained review, testing, release, or security systems.
Review policy, controls, model selection, context usage, agent loops, and spend attribution.
Deliver executive summary, evidence, roadmap, and scale / fix / stop recommendations.
This audit is not about whether developers like the tool. It checks whether AI-assisted coding is improving the delivery system: PR flow, review economics, quality controls, governance, and measurable throughput to production.
DORA shows delivery outcomes. This audit connects those outcomes to AI adoption, review load, governance controls, and cost attribution so leaders can decide whether to scale, fix, or stop parts of the rollout.
No by default. The audit works from delivery metadata, workflow data, PR/review patterns, AI-tool telemetry where available, governance artefacts, and stakeholder interviews. Source-code access is not required unless explicitly agreed.
Yes. Public claims are intentionally conservative because much of the relevant work was done inside large enterprise environments. Validation is available under NDA where appropriate.
If there is a fit, we agree scope, data access, stakeholders, and timeline. The audit runs for four weeks and ends with a board-ready decision pack and practical roadmap.
It is not for teams still debating whether to try coding AI, organisations looking for generic DevOps consulting, or companies that only want tool training. It is designed for engineering leaders who already have coding AI in use and need evidence about delivery impact.
View a redacted decision pack, read the audit outline, or book a 15-minute fit review if the problem is already clear.