I help enterprises build AI systems that pass production scrutiny, not just demos — through structured assurance, outcome-based QE transformation, and the governance infrastructure boards can rely on.
This is the shift now underway across enterprise AI delivery — from testing bolted onto the end of a release to trust engineered into every stage of it, backed by evidence a board or regulator can rely on.
Test as a phase — bolted on after development
Trust engineered into every stage, not bolted on
AI outputs taken on faith
AI outputs verified, evidenced, and defensible to the board and regulator
Tooling silos, ad-hoc AI pilots, no audit trail
Unified AI-QE platform with built-in governance and audit-ready evidence
Different seat, same underlying question: is the organisation's AI-led delivery something it can actually stand behind.
The board is asking hard questions about reliability, bias, and regulatory exposure — and a compliance audit or a public failure would be costly.
The operating model needs a reset — not another tool. Test cycles are still slow, defect leakage persists, and the business is asking why AI hasn't made delivery faster or cheaper.
Nobody wants to ship the hallucination, the leak, or the jailbreak that makes the news. LLM and agentic features are reaching real users faster than the assurance discipline around them is maturing.
Each pillar can be engaged standalone or combined. Every one maps to work I have personally led and held P&L accountability for.
Know it won't hallucinate, leak data, or fail silently in front of a customer. For teams shipping LLM features or agentic products — the flagship engagement, built on the TrustQE platform itself.
Senior QE leadership without a full-time hire. An ongoing seat covering operating model, delivery reliability, and AI-led QE strategy.
For practices where AI tools haven't moved the economics yet — or that are starting from zero, built AI-native from day one.
Defect leakage and cycle time become forecastable, not guessed.
40%+ throughput gain from AI-native delivery pods.
Every release is checked and proven safe before it reaches customers.
From the AI-POD operating model — measured across live customer delivery portfolios, not a lab benchmark.
Delivery productivity uplift via AI-POD models, embedded into live enterprise programs.
Reduction in defect leakage — from structural shift-left and autonomous test generation.
Cycle-time reduction with continuous AI — autonomous agents integrated directly into the delivery pipeline.
Cost reduction from AI-optimized test execution — fewer manual cycles and redundant runs across the QE lifecycle.
Redundant tests — AI-optimized coverage runs the right tests, not just more of them.
Start where it matters most for your organisation. Each path is a distinct engagement with a clear scope and a defined outcome.
A credible outside view on what needs to happen next — before committing to anything larger.
Take the free 5-minute snapshot →A costed transformation roadmap — target operating model and execution sequence, from a structured QE and AI-led delivery assessment.
Delivered through the journey, not handed over as a document. AI-POD delivery, autonomous test generation, Digital QE Twins, platform deployment.
The P&L discipline of someone who has run the function at enterprise scale. Ongoing leadership across operating model, delivery reliability, and AI assurance strategy.
I built TrustQE to make structured AI assurance practical at enterprise scale — seven testing modules, one platform. Web UI and CLI. Provider-agnostic. CI/CD-ready.
No signup needed to try it. Full platform trial access is coming soon.
If your AI programme has a trust gap — in the board, the regulator, or the market — I can tell you within 45 minutes whether I can close it.
Prefer LinkedIn? Message me there instead.