Your developers are
writing code faster.
Your delivery system
is not.
I run fixed-scope AI Delivery System Audits for engineering organisations using Copilot, Cursor, Claude Code, Codex, and internal agents. The audit shows whether AI is improving cycle time and delivery outcomes, or just increasing PR review load, governance risk, and spend.
From £18,000 fixed fee · 4 weeks · 50% on signature, 50% on delivery
No form, no signup. The outline opens instantly. You can also inspect sample findings before booking.
Matt Drankowski, AI-SDLC Architect
Principal-led AI delivery diagnosis. You work directly with me on every engagement: no account managers, no junior consultants, no retainer pressure.
Proof focused on AI-assisted delivery
Governance · AI-SDLC measurement · production delivery change
AI-generated PR flow · reviewer load · cycle-time bottlenecks
Risk posture · review controls · expansion readiness
Built for mid-market engineering orgs. Validated at enterprise scale. Fixed-fee, principal-led, async-first.
Client names anonymised due to enterprise confidentiality. Validation available under NDA.
AI adoption is not the same as engineering performance.
Most AI coding deployments optimise for individual developer adoption, not for measurable delivery performance. Here’s what that creates in practice.
The divergence your AI adoption metrics aren’t showing you
After AI coding tool deployment, individual output and organisational delivery decouple. Most engineering leaders don’t see it until licence renewal.
Delivery
Gap
More generated code does not automatically mean faster delivery
AI coding tools increase AI code review load and change size. AI code governance, testing assurance, and release control still require your most senior engineers. AI-to-production cycle time stays flat, or gets worse.
Usage metrics show accelerating code output. AI delivery metrics tell a different story. The bottleneck moves from writing to reviewing, and it compounds quietly.
Without SDLC guardrails, AI amplifies inconsistency, rework, and security risk
AI-assisted code introduces specific vulnerability patterns: outdated defaults, insecure dependencies, hallucinated libraries. Without quality gates and review standards, security exposure compounds.
Most engineering teams have no documented AI governance posture. Enterprise buyers and auditors are starting to ask. No posture means delayed contracts, or lost ones.
Token and inference costs grow silently until they appear on the quarterly bill
AI agents, large context windows, and unoptimised model selection generate significant inference spend. Without attribution and governance, the cost is invisible until it hits the budget review.
Leaders need to know whether AI is improving the engineering system or just generating more work at rising cost. That answer requires visibility across delivery, governance, and spend, not assumptions.
Four pillars. One clear picture.
A structured diagnostic across the areas that determine whether AI is improving your engineering system, or just adding activity to existing constraints.
Delivery Performance
Assess AI delivery indicators, AI-to-production cycle time, AI-generated PR flow, governance review depth, release friction, and bottlenecks across your AI-assisted delivery lifecycle. Understand where AI coding tools are improving the system, and where they are not.
AI Adoption Quality
Understand how developers actually use Copilot, Claude, ChatGPT, agents, IDE tools, and internal AI workflows, and where usage creates real leverage versus noise or rework.
Governance & Security
Review standards for AI-assisted code, data handling, review expectations, secure usage policies, quality gates, and alignment with your compliance requirements.
Token & Inference Economics
Identify waste in model selection, context size, prompt design, agent loops, routing, caching, and task-to-model fit. Understand where inference spend is justified and where it isn’t.
The 4-week AI Delivery System Audit
A fixed-scope AI-SDLC diagnostic that maps whether AI coding tools and agents are improving organisational delivery or just increasing code volume, review load, governance pressure, security exposure, and spend. This is not a DevOps maturity assessment, cloud modernisation programme, or generic AI strategy engagement.
- Executive summary: CTO / VP Engineering version of the evidence, risks, and recommended decisions
- Delivery bottleneck map: AI performance indicators, AI-to-production cycle time, AI delivery throughput, AI-generated PR flow, governance review depth, and release friction
- AI governance gap register: AI code standards, data handling, quality gates, policy alignment, and secure usage
- Inference waste snapshot: Model selection, context size, agent loops, caching, routing, and spend attribution
- 30/90-day roadmap: prioritised quick wins and strategic fixes tied to measurable AI engineering ROI
- Scale / fix / stop recommendation: a clear leadership view before renewals, audits, or expansion
- 30-day async follow-up included
No form. Opens instantly. Print/PDF-ready. Or request principal review if you would like a tailored response.
The principal who led enterprise-scale AI adoption is the principal who delivers your audit. No account managers. No junior consultants. No retainer pressure. One person, end-to-end, from intake to roadmap.
A four-week fixed-scope engagement
Each phase has a defined output. You know exactly what you are getting and when.
Baseline
Map your tooling stack, AI performance indicators, PR and review flow, AI usage patterns, and team structure. Establish the measurement baseline before any analysis begins.
Bottleneck analysis
Identify where AI is adding output into existing bottlenecks rather than removing them. AI governance review queues, quality gates, security exposure, governance gaps, and delivery constraints.
Governance and cost economics
Review AI usage policy, token and inference spend, model selection, prompt efficiency, agent loop design, and workflow cost attribution. Surface waste and coverage gaps.
Roadmap delivery
Deliver quick wins, strategic fixes, and an executive summary with a prioritised implementation roadmap. A clear view of what to scale, what to fix, and what to stop.
What the audit delivers
A clear, evidence-based picture of where AI is helping the delivery system, where it is creating drag, and what to do about it.
- Baseline where AI is actually helping delivery, not just where adoption metrics look good
- Identify review, testing, security, and governance bottlenecks introduced or amplified by AI tooling
- Detect token and inference waste across AI-assisted workflows and agentic delivery systems
- Prioritise quick wins alongside strategic fixes across all four audit pillars
- A practical roadmap for measurable AI engineering ROI, tied to delivery outcomes
- Give leadership a clear view of what to scale, what to fix, and what to stop
What a board-ready finding looks like
The audit does not stop at "AI usage is up". It turns delivery evidence into a concise decision view: where the bottleneck moved, what risk is exposed, and what leadership should scale, fix, or stop next.
Built for senior engineering leaders at 200–5,000 engineer scale
The audit is designed for organisations where AI coding assistants and agents are already deployed, and where leadership needs evidence before renewal, rollout expansion, board reporting, or governance decisions.
Engineering and AI Delivery Leadership
CTOs, VPs of Engineering, Heads of AI Transformation, Engineering Directors, Heads of Software Engineering, and senior leaders accountable for AI-assisted delivery outcomes.
200+ Engineers, AI Tools Already Running
Engineering-heavy organisations already using or piloting GitHub Copilot, Cursor, Claude Code, Codex, ChatGPT Enterprise, internal agents, GitLab Duo, JetBrains AI, or similar coding tools. Regulated, quality-sensitive, or complex software environments.
AI Deployed. ROI Unclear.
You have AI coding assistants in use and developers who feel faster, but delivery metrics haven’t moved in the way you expected. You need a credible, evidence-based view of what’s actually happening across delivery, governance, and cost.
Outside the Audit Scope
Not for cloud migration, managed DevOps, platform rebuilds, generic AI strategy workshops, or teams that have not yet deployed AI coding tools.
Zero-Friction Operational Framework
The audit is designed to work from existing delivery, review, governance, and cost evidence with minimal calendar drag.
Zero Management Overhead
I do not require a seat in your daily standups or a manager to assign me tasks. I operate autonomously against an agreed statement of work, surfacing blockers directly to the principal sponsor, not the delivery team.
Asynchronous by Design
Progress is delivered via structured weekly executive summaries and clear technical documentation. Your engineers receive no calendar invitations and no Slack noise unless they are the direct owners of a decision point.
Tool-Agnostic Integration
I work from the systems you already use for delivery evidence: Jira, GitHub Enterprise, GitLab, CI logs, review metadata, AI tool telemetry, governance artefacts, and policy documents.
AI-SDLC proof, not adjacent consulting
Representative work from AI-assisted delivery, review-flow, and governance engagements. Clients anonymised at their request.
A large engineering organisation with AI tool licences deployed, individual coding speed improving, and unresolved questions about governance, review flow, delivery measurement, and production adoption.
Architecture and delivery of the enterprise Copilot adoption operating model: AI-SDLC measurement, governance frameworks, review expectations, safe-use controls, and production delivery change.
Full production at enterprise scale with a defensible governance posture and delivery measurement model. Validation available under NDA.
AI adoption looked healthy in usage dashboards, but delivery leaders could not explain why cycle time had not improved in line with developer sentiment.
AI-SDLC measurement surfaced larger PRs, concentrated reviewer load, and weak attribution between generated code, review effort, and production outcomes.
The renewal conversation moved from seat activation to retained delivery value, review policy, and the specific changes needed before scaling usage further.
Engineering leaders were using AI coding tools, but legal and information-security stakeholders needed a clearer posture before approving broader use.
Created a practical governance gap register covering acceptable use, data handling, review expectations, provenance, vendor evaluation, and auditability controls.
Leadership moved from "we use Copilot" to a documented risk posture and board-ready control story for AI-assisted software delivery.
Enterprise AI Delivery, Built for Production Environments
Many AI engagements stop at pilots, demos, or policy decks. My work focuses on the operating system underneath: SDLC architecture, governance, delivery metrics, and cost control.
of enterprises have an AI pilot running · reach sustained production
The Production Gap is not a technology failure. It is a governance, SDLC architecture, and organisational design failure, and it is precisely what I have been hired to close at enterprise scale.
Sources: Gartner, 2024-2025 enterprise AI surveys · CIO.com, 2025 analysis of AI pilot-to-production rates · industry range 80–90% of AI pilots fail to reach sustained production
A Standard Built Where Failure Has Consequences
I architected AI systems in environments where an untested output is a liability event, not a simple bug. That standard defines every engagement: governance architecture precedes AI delivery throughput. It is the difference between a resilient enterprise AI deployment and one that collapses after consultants leave.
Closing the Production Gap: Proven at Scale
The gap between pilots and production cannot be closed by simply buying tools. It requires fixing the underlying organisational systems. I have taken enterprise AI adoption from rollout to production, and I know where these deployments break.
Governance Built to Withstand Scrutiny
From North American NIST AI RMF alignment to UK DSIT and ICO requirements, I design for regulatory durability across both markets. The output is a governance framework built to withstand scrutiny from enterprise buyers, legal teams, and auditors, not just a pilot posture.
Implementation support after the diagnosis
The audit is the starting point. Once the bottlenecks are visible, any follow-on work is scoped from the evidence, not chosen from a generic service menu.
How this works: The AI Delivery System Audit surfaces exactly where to focus. Follow-on support is optional and evidence-led: governance, review flow, agentic workflow design, or AI spend attribution.
AI Engineering Governance Implementation
Establish a documented, defensible AI governance posture. Covers NIST AI RMF alignment, OWASP LLM Top 10 security controls, acceptable use policy, IP and copyright exposure review, quality gate design, and audit-ready technical documentation.
Rolling From £15,000 · or £4,000/mo rollingAI-SDLC Adoption Strategy
Design a structured AI coding adoption strategy: deployment planning, usage standards, enablement, and adoption metrics that connect to delivery outcomes rather than seat activation.
4-6 Weeks From £12,000 · fixed feePR Review Automation & AI-Assisted Quality Gates
Reduce governance pressure by automating quality gates calibrated for AI-generated code patterns. Covers SAST configuration, automated PR standards enforcement, reviewer routing, and AI workflow tooling to reduce senior engineer bottlenecks.
3-4 Weeks From £10,000 · fixed feeAgentic SDLC Workflow Design
Design and implement agentic engineering workflows using spec-driven development. Shift the source of truth to formal specifications so AI agents generate correct, context-aligned code. Eliminates LLM rework and reduces context rot in multi-agent delivery systems.
6-8 Weeks From £18,000 · fixed feeToken & Inference Optimisation Sprint
Identify and eliminate waste in AI inference spend. Covers model selection, context size right-sizing, prompt design, agent loop efficiency, caching, and routing strategy. Spend is attributed by team, feature, or workflow so ROI is measurable.
3-4 Weeks From £8,000 · fixed feeFractional AI Engineering Advisor
Senior AI engineering guidance without the full-time headcount cost. 1-3 days per month covering tooling strategy, vendor evaluation, SDLC architecture decisions, and board-level AI ROI reporting, for organisations that need enterprise-grade perspective at advisory scale.
Ongoing From £3,500/mo · 1 day/monthAI Spend Attribution Advisory
Make coding-agent and LLM spend visible by team, workflow, feature, or task. Covers model routing, caching, context discipline, and dashboards that connect AI cost to shipped outcomes.
3-4 Weeks From £8,000 · fixed feeWhat clients say
Institutional engagements. Corporate names are redacted to protect proprietary source code architecture, trade secrets, and ongoing regulatory postures. Full identity validation is available under reciprocal NDA during onboarding.
"Matt didn't just deploy GitHub Copilot, he redesigned how our engineering organisation reviews and ships AI-generated code. Adoption went up, but so did our AI delivery performance indicators. That combination is genuinely rare."
"We thought our AI investment was paying off until Matt showed us the AI-SDLC Measurement numbers. Senior engineers were spending 40% more time in code review. He fixed the AI workflow in two weeks and the change was immediately visible in our AI-to-production cycle time metrics."
"Matt's AI governance framework gave us the structure our legal and infosec teams needed before our board presentation. We went from 'we're using Copilot' to having a documented risk posture, auditability controls, and a vendor evaluation policy. Night and day."
I help engineering leaders prove whether AI is improving delivery, not just increasing code volume.
I’m Matt Drankowski, AI-SDLC Architect. I operate through a UK-registered company and am personally based in Poland, working remotely with engineering leaders across the UK, Europe and the US. I help organisations where AI coding assistants and agents are already deployed, but delivery metrics haven’t moved in the way expected, and the ROI case is unclear.
The hard part isn’t the tooling. My most significant enterprise engagement was architecting GitHub Copilot adoption at large-enterprise scale. That work proved technical rollout is the straightforward part. The real challenge is resolving AI workflow constraints, establishing governance, and proving delivery impact, all while navigating security, compliance, and organisational complexity.
The lens is AI-SDLC. I focus on how AI coding tools and agents change review flow, delivery measurement, governance posture, and spend attribution. My background in enterprise software delivery helps me read the system, but the front-of-house offer is a fixed-scope AI delivery diagnosis.
Validate my background on LinkedIn before booking a fit review.
Secondary tools if you are still exploring.
These tools remain available for early self-assessment. The main path for outbound visitors is still the audit outline, sample findings, and fit review.
AI coding ROI calculator
A browser-based model to isolate retained engineering delivery value from raw local typing acceleration. Input your team size, AI delivery metrics, AI tool spend, and AI code review load to surface review drag, cost waste, and retained ROI.
Run the ROI calculatorUK & US AI Governance Readiness Assessment
A 40-point diagnostic covering NIST AI RMF alignment, OWASP LLM Top 10 security controls, IP and copyright exposure, AI Literacy posture, vendor risk, human oversight, and incident response readiness. Calibrated for UK and US enterprise buyers and auditors. Identify your gaps before they identify you.
Access the Governance AssessmentFrequently asked questions
Tool adoption is not the same as organisational delivery improvement. The audit shows whether AI is reducing AI-to-production cycle time or simply shifting work into AI governance review queues, rework, security exceptions, governance gaps, and hidden spend.
No. Delivery metrics show outcomes. It usually does not show whether AI coding tools are adding load to senior reviewers, changing AI-generated code risk profiles, creating policy exceptions, or increasing inference spend without attribution.
No. This is a fixed-scope engineering audit focused on delivery flow, AI-SDLC measurement, governance, security controls, and AI tooling cost. It is not an AI strategy deck, prompt-training workshop, or open-ended retainer.
No. This is not a DevOps maturity assessment, cloud migration, managed platform rebuild, or infrastructure cost programme. The audit measures how AI coding tools and agents change review flow, governance, delivery risk, AI-to-production cycle time, and system-level ROI.
Enterprise intake for 2026 is currently open. You can start asynchronously by requesting the 2-page audit outline, or book a 15-minute fit review if the problem is already clear. If there is a fit, I scope a fixed engagement within 48 hours. No lengthy proposals, no retainer pressure, just a clear diagnosis and path forward.
I operate on fixed-scope, fixed-price engagements. The front-end offer is a 4-week diagnostic to establish your AI delivery baseline and measure the hidden bottlenecks. Follow-on implementation phases are optional and scoped separately from the evidence. I do not do body-shopping or open-ended retainers.
Yes. I build AI governance frameworks calibrated to UK and US enterprise requirements, covering NIST AI RMF alignment, UK DSIT and ICO guidance, OWASP LLM Top 10 security controls, IP and copyright exposure from AI-generated code, and audit-ready documentation. The output is a defensible posture your legal, infosec, and board teams can sign off on before enterprise buyers or auditors ask.
Yes, under reciprocal NDA. Most enterprise engagements are confidential due to proprietary source code architecture, ongoing regulatory posture, and competitive sensitivity. Two prior clients have authorised named references in the testimonials above; for the rest, identity validation and reference calls are available during onboarding once a mutual NDA is in place. If references are a hard requirement before booking the fit review, mention it on the call and I will arrange a redacted reference call with a comparable engagement profile.
UK-registered AI-SDLC consultancy for UK, EU and US engineering leaders. I’m personally based in Poland and work remotely with clients across the UK, Europe and the US. Most audit work is async-first, with scheduled leadership sessions in UK/EU/US-friendly time zones.
Default is read-only, least-privilege access scoped to the specific bottleneck under review. I do not require access to source code to deliver the audit — delivery flow, governance posture, and cost attribution can be assessed from PR metadata, automation logs, and AI tool telemetry. Where deeper code review is needed, it is conducted on a synthetic, redacted sample under NDA. Your IP stays yours; deliverables are produced for you, not derived from your assets.
Yes. The most significant enterprise engagement to date was in a safety-critical industrial environment with formal regulatory oversight. I am comfortable operating within ISO 27001, SOC 2, and sector-specific control environments (FCA / PRA for UK financial services, HIPAA-adjacent workflows for US healthcare, public-sector data handling requirements). Audit deliverables can be produced in formats compatible with your internal risk register, vendor assessment, and regulatory reporting cycles.
Yes — and that is the point. The audit is diagnostic, not advocacy. If your delivery performance, governance posture, and cost attribution are healthy, the deliverable will say so clearly with the evidence, and I will tell you which follow-on engagements are not worth commissioning. A clean audit result is a defensible board-level asset in its own right; an audit that always recommends more work is not an audit.
Disagreement between engineering, security, AI programme, and finance leadership is one of the most common engagement triggers — it usually means the bottleneck is organisational, not technical. The audit is designed to surface the evidence each stakeholder needs in their own language: delivery metrics for engineering, control and risk posture for security, spend attribution and unit economics for finance and the board. The 30-day async follow-up is explicitly available to support internal alignment off the back of the findings.
Yes. Every engagement is delivered 100% remote and async-first by design — no travel, no on-site requirement, no recurring meetings on your calendar. I operate in UK / EU / US timezones and overlap with all three for synchronous escalations when needed. The 15-minute fit review, the weekly executive summary, and the final readout are the only fixed synchronous touchpoints; everything else is documented and asynchronous. Your engineers receive no calendar invitations unless they own a specific decision point.
The 30-day follow-up is included with every audit. It covers async review of the implementation steps you take in the first month after delivery — typically clarifying findings, pressure-testing the prioritisation of the 30/90-day roadmap, and answering technical questions as your team begins executing. It is not a second engagement; it is structured protection against the most common failure mode, which is the audit landing cleanly and then stalling in implementation.
Three concrete differences. First, principal-led end-to-end: the person scoping the engagement is the person delivering it — no handoff to a junior team after the sale. Second, engineering-specific evidence base: the audit reads your actual delivery flow, PR metadata, governance queue depth, and AI tool telemetry rather than running stakeholder interviews and producing a strategy deck. Third, async-first delivery: no recurring weekly meetings, no junior-consultant slide production, and no retainer pressure after the engagement closes. If a Big 4 or strategy-house engagement is the right fit for your situation, I will say so on the fit review.
See what the audit actually looks like.
A short walkthrough of the methodology, the deliverables, and what a real AI delivery finding sounds like in a board conversation. If you prefer reading, the 2-page outline covers the same ground.
Visit the YouTube channelFind out whether your AI coding investment
is improving delivery, or just generating more work.
If the problem is already clear, book a short fit review. If not, start with the outline or sample findings first.
“Matt didn’t just deploy AI coding tools, he redesigned how our engineering organisation reviews and ships AI-generated code. Adoption went up, but so did our AI delivery performance indicators. That combination is genuinely rare.” - VP of Software Delivery, industrial systems group
See the audit outline, inspect sample findings, or validate on LinkedIn.