I help CTOs and VPs of Engineering transform their SDLC, clear the “Review Economics” bottleneck, and deploy secure, AI-assisted platform architecture that converts raw LLM velocity into bottom-line delivery.
As trusted by
GitHub Copilot · Review Economics tooling · DORA uplift
£400k+ annualised savings · AI FinOps · LLM cost attribution
GitHub Enterprise · AI-ready CI/CD · SAST at scale
EU AI Act compliance · Platform architecture · Cloud FinOps
AI coding tools solve the individual speed problem and create an organisational one. Here's what that looks like in practice.
The divergence your DORA metrics aren’t showing you
After AI tool deployment, these two metrics decouple. Most engineering leaders don’t see it until licence renewal.
AI tools increase average PR sizes by 20%. Those larger, faster-arriving PRs land in the queue of your most senior — and most bottlenecked — engineers. Your lead time isn't improving because the bottleneck moved from writing to reviewing, and your DORA metrics are masking it.
Research shows AI-assisted developers are 20–30% more likely to introduce security vulnerabilities. LLMs confidently generate outdated patterns, insecure defaults, and hallucinated dependencies. Most CI pipelines aren't configured to catch AI-specific failure modes — and attackers already know it.
Major EU AI Act obligations are now in effect, including Article 4 AI Literacy mandates. For DACH enterprises, Works Council (Betriebsrat) approval requirements add another layer — and § 87 BetrVG prohibits using AI tools for performance monitoring without co-determination. Most engineering teams have zero documented compliance posture.
Redesign your software delivery lifecycle around AI tools — DORA/SPACE baseline measurement, Review Economics fixes, and Automated Quality Gates calibrated to catch the vulnerabilities AI specifically introduces.
Article 4 AI Literacy programmes, Works Council-ready deployment frameworks for DACH enterprises, and a documented compliance posture before your audit window.
Invert your SDLC so formal specifications are the source of truth — not code. AI agents generate from specs, eliminating the ambiguity tax that forces LLMs to guess intent and produce rework. Feature delivery drops from days to hours.
27% of AI startup cloud budgets are wasted on idle or oversized LLM infrastructure. I cut inference spend 40–70% via AWS Inferentia2 (Inf2) migration and per-user token tracking — so you know exactly where AI ROI is real.
My most requested engagement for 2026. A 4-week fixed-scope audit of how AI tools are actually affecting your delivery performance — not how you think they are. I establish a DORA/SPACE baseline, quantify the hidden Review Economics cost, and implement Automated Quality Gates before I leave.
Free 30-min scoping call first.
No commitment, no pressure.
Watch the Masterclass on Agentic Coding
The session that filled to 2,000 engineers — now available as a free framework download. Learn the system that closes the AI Production Gap.
The Spec-Driven Engineering Framework
How to Close the AI Production Gap
Purpose-built for DACH enterprises (Germany, Austria, Switzerland) facing Works Council (Betriebsrat) scrutiny and EU AI Act audit windows. This rolling service delivers: Article 4 AI Literacy training for engineering teams; a Works Council-ready deployment framework satisfying § 87 BetrVG (no performance monitoring, full co-determination); technical documentation that survives regulatory inspection; and a compliance posture your legal and HR teams can sign off on before your auditors ask. The only offering in this space built by someone who has operated it inside a Fortune 100.
RollingStop writing code. Start writing specifications. This 6–8 week engagement implements the full SDD methodology — Specify, Plan, Decompose, Implement, Validate — where Markdown specifications become the authoritative source of truth. AI agents generate directly from spec, eliminating the Ambiguity Tax that forces LLMs to guess intent and produce rework, and solving Context Rot by keeping agents aligned to evolving intent without manual re-prompting. Outcome: feature delivery velocity increases 20x — from days to hours, spec to production.
6–8 WeeksCut LLM inference costs 40–70% via AWS Inferentia2 (Inf2) migration. Predictive token tracking with AWS Lambda attributes spend to users, teams, or features — so AI ROI is measurable, not assumed.
3–4 Weeks1–3 days per month of embedded advisory. AI tooling strategy, vendor evaluation, SDLC architecture decisions, board-level AI ROI reporting, and hands-on implementation when the work demands it.
OngoingHigh-impact workshops that move engineering teams, managers, and leadership from AI-curious to AI-effective. From GitHub Copilot fundamentals to responsible AI governance — for audiences of 10 to 2,000+. Every session is tailored, hands-on, and ready for Works Council sign-off.
Half / Full DayReal numbers from real projects. Clients anonymised at their request.
A 7,000-person engineering organisation with AI tool licences deployed, individual coding speed improving — and organisational lead time flatlining. No governance framework, no Review Economics tooling, no SDLC redesign. Pilots were running. Production was not being reached.
End-to-end architecture and delivery of the enterprise GitHub Copilot rollout: governance frameworks, AI Literacy training at scale, Review Economics tooling to resolve the senior reviewer bottleneck, and Automated Quality Gates calibrated for AI-authored code vulnerability patterns.
Full production at 7,000-engineer scale. DORA improvement across all four indicators: deploy frequency, lead time, change failure rate, and MTTR. The only enterprise AI rollout at this scale confirmed to have simultaneously closed the Review Economics gap.
A Fortune 500 Agriculture enterprise paying for cloud infrastructure it couldn’t see clearly. AI and ML workloads were compounding the waste — idle inference instances, oversized LLM deployments, and no attribution of which teams or features were driving spend.
Full cloud cost audit: rightsizing, Reserved Instance optimisation, zombie resource cleanup. LLM inference cost tracking via AWS Lambda with attribution by team, user, and feature. Spend dashboards that surfaced the real AI ROI gap and made it impossible to ignore.
30% cloud cost reduction. £400k+ in annualised savings. Infrastructure spend became a managed variable rather than a mounting liability. Dashboards and attribution tooling ensured savings persisted long after handover.
An entire engineering workforce operating on fragmented toolchains — inconsistent branching strategies, no SAST, no standardised CI/CD. Introducing AI coding tools on this foundation would have accelerated entropy and made Review Economics collapse inevitable.
Full engineering workforce migration to GitHub Enterprise: standardised CI/CD pipelines, branch protection rules, SAST tooling calibrated to catch AI-specific vulnerability patterns. The secure architectural foundation required before any safe AI-assisted development at scale.
100% engineering workforce on a standardised, AI-ready delivery platform. Zero Review Economics collapse during the subsequent AI tool rollout. The governance and tooling foundation that made the safety and compliance story possible.
Most AI consultants have observed enterprise AI from a safe distance. I've operated it in environments where failure is not a sprint-retrospective item — it's a liability event.
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 was hired to close at Fortune 100 scale.
I architected AI and automation systems at a Fortune 100 North American Construction Equipment Manufacturer — where the machines operate on active job sites and an untested AI output is a liability event, not a code smell. That operating standard is non-negotiable, and it defines every engagement I run: governance architecture before velocity, always. It is the difference between a rollout that holds and one that quietly collapses six months after the consultant leaves.
79% of enterprises are running AI pilots. 11% reach production. The gap does not close by adding tools — it closes by fixing the organisational system those tools land in. The 7,000-engineer Fortune 100 rollout I led reached full production with measurable DORA improvement across all four indicators: deploy frequency, lead time, change failure rate, and mean time to recovery. I have been on the other side of this gap. I know exactly where and why it breaks.
From North American safety-critical systems to EU AI Act Article 4 AI Literacy obligations, DACH Works Council § 87 BetrVG co-determination requirements, and GDPR-aligned AI governance frameworks — I design for regulatory durability, not just technical capability. Generic AI consultants optimise for rollout speed. I optimise for systems that are still functioning correctly after your auditors, Works Council, and legal team have finished asking questions.
"Matt didn't just roll out GitHub Copilot — he redesigned how our engineering organisation reviews and ships AI-generated code. Adoption went up, but so did our DORA scores. That combination is genuinely rare."
"We thought our AI investment was paying off until Matt showed us the Review Economics numbers. Senior engineers were spending 40% more time in code review. He fixed the pipeline in two weeks and the change was immediately visible in our lead time metrics."
"The EU AI Act compliance work was exactly what we needed before our Works Council review. Matt understood the Betriebsrat requirements cold and built a deployment framework our legal and engineering teams could both sign off on."
I’m Mateusz ‘Matt’ Drankowski — Agentic AI Architect and Fractional AI Officer based in Kraków, Poland. I design AI-native engineering systems for enterprises where pilots are already running, organisational lead time isn’t improving, and the Production Gap is becoming a board-level concern.
My most recent enterprise role was architecting and leading the GitHub Copilot and AI Assistant rollout across a Fortune 100 organisation. That work taught me that the technical implementation is the easy part. The hard part is fixing Review Economics, establishing governance, running AI Literacy programmes at scale, and measuring real lead time impact rather than individual typing speed — and doing all of it in a way that survives audit and Works Council review.
I carry 13+ years of AWS and platform engineering experience into every engagement — from LLM inference cost optimisation on AWS Inferentia2 to EU AI Act Article 4 compliance and Works Council deployment frameworks for DACH enterprise clients.
Both tools are free. Most teams find the numbers more surprising than expected.
Input your team size, current DORA metrics, AI tool spend, and average PR size — and see whether your productivity gains are real or masked by Review Economics overhead. Includes the Code Volume Inflation model showing where your 55% typing speed gain is actually going.
Get the CalculatorA 40-point checklist covering Article 4 AI Literacy obligations, high-risk system classification, technical documentation requirements, and Works Council readiness for DACH enterprises. Updated for current EU AI Act timelines — use it to know exactly where your gaps are before an auditor does.
Get the ChecklistWhen AI tools increase individual coding speed, they typically increase PR sizes by 15–20%. Those larger, faster-arriving PRs land in the review queue of your most senior — and most expensive — engineers. Your developers are faster; your review pipeline isn't. The result is flat or worsening organisational lead time despite real improvements in individual output. Review Economics is the discipline of measuring and resolving that bottleneck — it's the difference between AI making your organisation faster vs. making individuals faster while the organisation stalls.
Yes — DACH (Germany, Austria, Switzerland) and Nordic (Sweden, Denmark, Norway, Finland) are my primary markets. I understand both the regulatory landscape (EU AI Act, GDPR, Works Council requirements under § 87 BetrVG) and the engineering culture. Based in Kraków, Poland (CET/CEST), I cover EU business hours naturally. I also work with UK and US enterprises — particularly those with European subsidiaries navigating EU AI Act compliance obligations for their engineering teams.
Article 4 requires organisations deploying AI systems to ensure staff have sufficient AI literacy to understand the capabilities, limitations, and risks of the tools they use — including AI coding assistants. This is not just a training checkbox. It requires documented competency assessment, a governance framework, and for DACH enterprises, a Works Council-approved deployment plan ensuring AI tools aren't used for performance monitoring, as required by German labour law (§ 87 BetrVG). Organisations without this documentation are exposed in any future audit or Works Council dispute.
SDD uses machine-readable specifications — not prose Jira tickets — as the authoritative source of truth. A formal spec defines behaviour, contracts, and acceptance criteria in a structured format that AI agents can consume directly. Instead of an LLM guessing intent from a description, it generates code from a deterministic spec — dramatically reducing rework and eliminating hallucinated logic. Typical outcome: feature implementation time drops from days to hours. I build the full toolchain: spec format, agent pipeline, output validation, and CI integration to verify spec conformance automatically.
I’m accepting 1 new engagement for Q3 2026. I cap active engagements deliberately to protect the quality of every client’s work — so book a strategy audit this week if your timeline is urgent. The call typically happens within 2–3 business days. We spend 30 minutes on your current AI tooling stack, delivery metrics, and regulatory exposure. If there’s a clear fit, I scope a fixed engagement within 48 hours. No lengthy proposals, no retainer lock-in upfront.
Book a free 30-minute strategy audit. We’ll map your current AI tooling, your DORA metrics, and exactly where Review Economics is eroding your velocity. No pitch deck. No retainer pressure. Just a clear diagnosis.
“Matt didn’t just roll out GitHub Copilot — he redesigned how our engineering organisation reviews and ships AI-generated code. Adoption went up, but so did our DORA scores. That combination is genuinely rare.” — VP of Engineering, Fortune 100
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