Prove whether faster coding
is becoming faster delivery.
A 4-week AI Delivery System Audit for CTOs, VPs Engineering, and AI transformation leaders who need evidence before licence renewal, rollout expansion, governance review, or board reporting.
In four weeks, I turn delivery data, PR/review flow, AI-tool telemetry, governance controls, and spend signals into a board-ready scale / fix / stop decision pack.
From £18,000 fixed scope · 4 weeks · final fee varies only with organisation scale and data-source complexity
Read the 2-page audit outline or validate the background on LinkedIn.
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.
Representative ranges from prior diagnostic and optimisation work; not a guaranteed result.
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.
More code is entering the system. Delivery isn’t moving.
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AI-assisted code introduces specific vulnerability patterns: outdated defaults, insecure dependencies, hallucinated libraries.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.
Representative ranges from prior diagnostic and optimisation work; not a guaranteed result.
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. When ready, .
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.
Evidence-led work, with validation available.
Representative decision patterns from confidential AI-assisted delivery engagements. Claims are kept to what can be supported; identity validation is available after fit confirmation under NDA.
Copilot adoption was expanding across a large engineering organisation while delivery measurement, review expectations, and governance controls still needed an operating model.
Adoption telemetry, delivery flow, review practices, safe-use requirements, governance artefacts, and production rollout constraints.
A production operating model for AI-assisted delivery: measurement, review expectations, governance controls, and accountable rollout decisions.
Engagement and role validation available after fit confirmation under reciprocal 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.
PR size and flow, reviewer concentration, cycle-time baselines, AI usage telemetry, and attribution between generated code and production outcomes.
AI-assisted output was entering the same constrained senior-review pool, so local coding gains were not translating cleanly into system-level delivery improvement.
Change review policy and routing before expanding seats; evaluate retained delivery value rather than licence activation alone.
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.
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 generic DevOps maturity consulting, cloud migration programmes, tool training, Copilot enablement workshops, developer sentiment surveys, generic AI strategy decks, 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.
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.
Many enterprise AI pilots never become sustained production systems. The failure point is rarely the model alone; it is the delivery system around it.
That 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.
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
After the AI Delivery System Audit, optional implementation support can be scoped from the evidence — governance controls, review flow, agentic workflow design, or AI spend attribution. Follow-on work is optional and never assumed.
What engagements like this typically surface
Not public client endorsements. These are representative, paraphrased summaries of feedback from confidential engagements, not verbatim quotes attributed to a named client. Corporate names are redacted to protect proprietary source code architecture, trade secrets, and ongoing regulatory postures. Engagement and role 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
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.
Explore AI delivery in practice.
Browse practical briefings on AI-enabled engineering, delivery economics, and governance. For the audit methodology and deliverables, the 2-page outline is the definitive guide.
Visit the YouTube channelMake the next AI investment decision
from delivery evidence.
Confirm whether the audit fits your organisation and decision timeline. The check takes about 60 seconds; the call takes 15 minutes.
From £18,000 · 4 weeks · fixed scope