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Systemic Engineering Velocity for Enterprise & High-Growth Scale-Ups

Your developers are
writing code 55% faster.
Your organizational velocity hasn’t moved.

AI tools accelerate individual typing speed while breaking your downstream software delivery lifecycle. I help European engineering leaders resolve the “Review Economics” bottleneck, secure compliance, and extract predictable delivery ROI from AI platform architectures.

Fortune 100 validated: Architected and led the GitHub Copilot & AI Assistant rollout for 7,000+ engineers, governance frameworks, Review Economics tooling, and sustained DORA uplift at a scale most consultants have never touched.
Try the AI Productivity Calculator

“Adoption went up, and so did our DORA scores. That combination is genuinely rare.” VP of Software Delivery, Fortune 100 Industrial Systems Group

Trusted by
Fortune 100 Enterprise Fortune 500 Agriculture Fortune 500 Manufacturing Global Theatre Platform Global Biotech

As trusted by

Fortune 100 · Heavy Industrial
7,000-engineer AI rollout

GitHub Copilot · Review Economics tooling · DORA uplift

Fortune 500 · AgTech Enterprise
30% cloud cost reduction

£400k+ annualised savings · AI FinOps · LLM cost attribution

Fortune 500 · Manufacturing
100% engineering standardised

GitHub Enterprise · AI-ready CI/CD · SAST at scale

Global Theatre & Life Sciences
+ 2 enterprise clients

UK & US AI governance · Platform architecture · Cloud FinOps

The AI productivity paradox

Your developers are faster. Your organisation is not.

AI coding tools solve the individual speed problem and create an organisational one. Here's what that looks like in practice.

Review Economics, visualised

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.

+55%
Code written per developer / week
↑ accelerating with AI tools
↔
Review
Economics
Gap
≈ 0%
Deployment lead time improvement
→ stalled at the senior reviewer bottleneck

What’s happening: AI tools accelerate code writing: but reviews still require your most senior, most bottlenecked engineers. PR volume grows 20%, queue depth compounds, and your AI licence cost scales monthly against flat delivery metrics. This is Review Economics. I architect the system fix.

Code Volume Inflation is choking your senior engineers

The Queue Bottleneck

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.

Hidden Cost

Your lead time isn't improving because the bottleneck moved from writing to reviewing, and your DORA metrics are masking it.

AI speeds up individual coding 55% · PR sizes up 20% · Organisational lead time: flat or worse

AI is writing code faster than your security posture can absorb

Vulnerability Threat

AI-assisted developers are 20–30% more likely to introduce security vulnerabilities—LLMs generate outdated patterns, insecure defaults, and hallucinated dependencies.

Enterprise Risk

For B2B SaaS vendors, your enterprise clients are asking whether their data is being pasted into public LLM prompts. One leakage incident fails your SOC 2 audit and loses you the contract.

20–30% higher vulnerability rate · IP data leakage exposure · SOC 2 / ISO 27001 audit risk

Deploying AI without a governance framework is an undocumented liability: most UK & US teams have no posture

Regulatory Reality

NIST AI RMF adoption is accelerating. The UK DSIT AI Framework and ICO guidance are tightening. Most engineering teams have zero documented AI governance posture when auditors or enterprise buyers ask.

Enterprise Buyer Pressure

UK and US enterprise clients are now requiring AI governance attestation in procurement. No documented posture means no contract. The exposure is commercial, not just regulatory.

NIST AI RMF · UK DSIT AI Framework · SOC 2 AI addendum · OWASP LLM Top 10
What I do

Systemic Engineering Velocity for the AI era

Closing the gap between local AI coding speeds and organisational lead times.

Baseline & Measurement

AI-SDLC Assessment

Measure the hidden Review Economics bottleneck. Establish a baseline for DORA/SPACE metrics before investing further in AI tooling.

Audit Engagement
Governance & Risk

AI Governance Framework

Establish a defensible AI governance posture for UK and US enterprises. Map exposure across NIST AI RMF, UK DSIT guidelines, OWASP LLM Top 10, IP risk, and responsible deployment controls.

UK & US Governance Assessment
Architecture & Delivery

Spec-Driven AgentOps

Shift the source of truth from code to formal specifications, allowing AI agents to generate correct software without the rework tax.

Read Whitepaper
FinOps & Efficiency

Inference Optimisation

Eliminate waste on idle or oversized LLM infrastructure. Cut costs by 40–70% via strategic model selection, workload right-sizing, and spend attribution.

Discuss Optimisation
7k+
Engineers on Fortune 100 AI rollout I led
55%
Faster local code output from AI, but lead time stays flat without SDLC redesign
40–70%
LLM inference cost reduction via workload right-sizing and spend attribution
27%
Of AI cloud budgets wasted on idle or oversized LLM resources
Signature Engagement

AI-SDLC Maturity Audit

Diagnostic Sprint: My most requested engagement for 2026. A 4-week fixed-scope audit mapping your true AI delivery performance. I establish your DORA baseline, quantify Review Economics bottlenecks, and implement Automated Quality Gates—moving at scale-up speed without enterprise bloat.

  • DORA & SPACE metric baseline, deploy frequency, lead time, change failure rate, developer satisfaction
  • Review queue audit and PR pipeline throughput analysis
  • Automated Quality Gates calibrated to the 20–30% vulnerability uplift from AI-authored code
  • PR size and review pipeline bottleneck analysis with remediation plan
  • AI tool governance framework, acceptable use, prompt standards, output validation
  • Prioritised roadmap to close the gap between local developer speed and organisational lead time
  • 30-day async follow-up support included
Fixed Scope
4 Weeks

Free 30-min scoping call first.
No commitment, no pressure.

2,000+ Engineers Attended

The Agentic Coding Session — Now a Free Download

The live session attended by 2,000+ engineers, distilled into a practical framework. Learn the system that closes the AI Production Gap.

The Spec-Driven Engineering Framework

How to Close the AI Production Gap

  • Ambiguity Tax: Markdown specs cut LLM rework to near-zero
  • Context Rot: agents stay aligned to evolving intent without manual re-prompting
  • 20× delivery velocity: spec to production in hours, not days
Download the Framework Visit my YouTube Channel
Execution tracks

After the audit, we build

Every strategic engagement begins with the AI-SDLC Maturity Audit, the 4-week diagnostic gateway. These are the specialized tracks your audit findings prescribe.

The Engagement Path: AI-SDLC Maturity Audit → Targeted Implementations (Compliance / SDD) → Fractional AI Officer. You don’t choose a track blind. The audit defines the exact priority and sequence.

UK & US AI Governance-as-a-Service

Purpose-built for UK and US enterprises that need a documented, defensible AI governance posture. This rolling service delivers NIST AI RMF alignment, OWASP LLM Top 10 security controls, AI Literacy training, IP and copyright exposure review, and audit-ready technical documentation. Secure legal, infosec, and board sign-off before your enterprise buyers or auditors ask.

Rolling

AgentOps Transformation: Spec-Driven Development

Stop writing code. Start writing specifications. This 6–8 week engagement shifts teams to the full SDD methodology (Specify, Plan, Decompose, Implement, Validate). Markdown specifications become the authoritative source of truth. By having AI generate code directly from specs, we eliminate LLM guesswork and solve context rot. Delivery drops from days to hours.

6–8 Weeks

AI-Cloud FinOps & Inference Optimisation

Cut LLM inference costs 40–70% via strategic model selection and workload right-sizing. Predictive token tracking with AWS Lambda attributes spend to users, teams, or features, so AI ROI is measurable, not assumed.

3–4 Weeks

Fractional AI Engineering Advisor

Fortune 100 AI engineering governance 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—designed for scale-ups that need enterprise-grade velocity without a £200k/year full-time hire.

Ongoing

AI Literacy Workshops

High-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 designed to satisfy board, legal, and employee consultation requirements.

Half / Full Day
How I integrate

Zero-Friction Operational Framework

Enterprise engagements succeed or fail on integration discipline. Here is exactly how I protect your team’s momentum.

01

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.

02

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.

03

Tool-Agnostic Integration

I plug directly into your existing enterprise stack—Jira, GitHub Enterprise, Azure DevOps, or AWS Organizations—without introducing new software vendor dependencies or procurement cycles.

Proof of work

Enterprise-scale results

Real numbers from real projects. Clients anonymised at their request.

Fortune 100 · Enterprise Technology
7,000
Engineers onboarded to GitHub Copilot & AI Assistants
The Situation

A 7,000-person engineering organisation with AI tool licences deployed, individual coding speed improving, and organisational lead time flatlining. No governance framework, no pipeline tooling, no SDLC redesign. Pilots were running. Production was not being reached.

The Complication

End-to-end architecture and delivery of the enterprise GitHub Copilot rollout: governance frameworks, AI Literacy training at scale, pipeline optimisations to resolve the senior reviewer bottleneck, and Automated Quality Gates calibrated for AI-authored code vulnerability patterns.

The Outcome

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.

Explore Fractional Advisory →
Fortune 500 · Agriculture
30%
Cloud cost reduction, £400k+ in annualised savings
The Situation

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.

The Complication

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.

The Outcome

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.

Explore Fractional Advisory →
Fortune 500 · Manufacturing
100%
Engineering workforce on standardised, AI-ready CI/CD
The Situation

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 crippled the review pipeline.

The Complication

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.

The Outcome

100% engineering workforce on a standardised, AI-ready delivery platform. Zero bottleneck collapse during the subsequent AI tool rollout. The governance and tooling foundation that made the safety and compliance story possible.

Explore Fractional Advisory →
Why this is different

Industrial-Grade Reliability in a Field
of Generic AI Consultants

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.

79% → 11%

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.

Fortune 100 · North American Construction Equipment · Safety-Critical Systems

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 velocity. It is the difference between a resilient enterprise rollout and one that collapses after consultants leave.

7,000 Engineers · Full Production · Sustained DORA Uplift

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 led a 7,000-engineer rollout to full production with sustained DORA improvements. I know exactly where enterprise rollouts break, and how to fix them.

UK & US AI Governance · NIST AI RMF · Multi-Jurisdictional

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. Generic consultants optimise for quick pilots. I build governance frameworks that withstand strict scrutiny from enterprise buyers, legal teams, and auditors.

Don’t take my word for it

What 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 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 Software Delivery, Fortune 100 Industrial Systems Group
7,000+ Engineer GitHub Copilot Deployment · DORA Score Improvement
✓ Verified Enterprise Engagement
★★★★★

"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."

Chief Technology Officer, Global B2B SaaS Platform
Review Economics Audit · 20,000+ MAU Production System
✓ Verified Enterprise Engagement
★★★★★

"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."

Head of Engineering, UK FinTech Scale-up
AI Governance Framework · Board-Level Risk Posture · NIST AI RMF Alignment
✓ Verified Enterprise Engagement
Matt Drankowski, Agentic AI Architect & Fractional AI Officer, Kraków, Poland
GitHub Copilot Enterprise AWS Solutions Architect Pro GitHub Advanced Security FinOps Certified
The person behind the work

I led AI adoption for 7,000 engineers at a Fortune 100. Now I architect the systems that close the Production Gap.

I’m 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.

The hard part isn't the tooling. My most recent enterprise role was architecting the GitHub Copilot rollout across a Fortune 100 organisation. That work proved technical implementation is the easy part. The real challenge is resolving pipeline bottlenecks, establishing governance, running AI Literacy programmes, and proving lead time impact—all while navigating security audit and compliance review.

Enterprise platform pedigree. I carry 13+ years of AWS and platform engineering experience into every engagement, from LLM inference cost optimisation and spend attribution to AI governance frameworks for UK and US enterprises navigating NIST AI RMF, IP risk, and responsible deployment mandates.

13+
Years AWS & platform engineering
7k+
Engineers on AI tools I've rolled out
UK / US
Primary markets
100%
Remote & async-first
Executive Diagnostic Tools

Quantify your structural risk vectors before scheduling an advisory consultation

Two proprietary analytical frameworks. Most organisations discover material gaps they were not tracking.

2026 AI Infrastructure ROI Model

An analytical model to isolate your actual engineering delivery velocity from raw local typing acceleration. Input your team size, DORA baselines, AI tool spend, and PR volume to surface your Review Economics exposure and Code Volume Inflation coefficient.

Access the Institutional Model

UK & 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 Assessment
Before you book

Frequently asked questions

Enterprise intake for 2026 is currently open. We begin with a 30-minute strategy audit to map your tooling stack, delivery metrics, and current bottlenecks. If there is a clear 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. Most transformations begin with a 4-week diagnostic to establish your DORA baseline and measure the hidden bottlenecks. Follow-on implementation phases—ranging from toolchain automation to governance frameworks—are scoped separately based on diagnostic findings. 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.

Ready to close your
Production Gap?

Book a free 30-minute strategy audit. We’ll map your current AI tooling, your DORA metrics, and exactly where pipeline bottlenecks are 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 Software Delivery, Fortune 100 Industrial Systems Group

Not ready yet? Access the executive diagnostic frameworks or email Matt directly