A fixed-scope 4-week audit for organisations already using Copilot, Claude, Cursor, or internal agents. See where AI is inflating PR review load, introducing governance risk, and increasing inference spend, then decide what to scale, fix, or stop in the next 30/90 days.
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 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.
Get the scope, expected inputs, deliverables, process, and fit criteria before booking time. The outline is also available as a print/PDF-ready page after submission.
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 CI/CD 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, CI/CD, 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.
Tool deployment is not the same as organisational ROI. The audit shows whether AI is reducing lead time or shifting work into review queues, policy exceptions, rework, and hidden spend.
No. DORA shows delivery outcomes. It usually does not show whether AI is moving work into senior review queues, security exceptions, governance gaps, or unattributed inference cost.
No. This is a fixed-scope engineering audit. Not an AI strategy deck, not training, and not body-shopping. The work focuses on delivery flow, review economics, governance, security controls, and cost.
The model is async-first. A typical engagement needs a sponsor, a small number of focused stakeholder interviews, access to agreed delivery/tooling evidence, and review of the final decision pack.
Client names and sensitive data stay confidential. Public proof is anonymised, and validation is available under NDA where appropriate. The audit does not require publishing internal metrics.
Start with something concrete. Request the audit outline, inspect sample findings, or run the calculator before booking time.