46%
of developers actively distrust the accuracy of AI-generated code — while 84% use it or plan to.
Diagnose
AI Code Assurance Diagnostic
Two weeks, fixed fee. A measured answer to a question most teams are guessing at.
The problem, stated precisely
Your team adopted AI coding assistants somewhere in the last eighteen months. Throughput went up — that part is visible in your delivery metrics. What is not visible is what happened to the defect profile underneath, because the code review process, the test suite and the security scanning were all designed for code written at human speed by humans who could explain it.
The published evidence says the gap is real and measurable:
56%
of AI-generated code passes a security review. Cross-site scripting passes 15% of the time.
19%
slower. Experienced developers in a randomised controlled trial using AI tools — while believing they had been 20% faster.
31%
of breaches now begin with vulnerability exploitation, surpassing stolen credentials for the first time in 19 years.
DORA's 2026 research found the same tension from the other direction: higher AI adoption correlates with both increased delivery throughput and increased delivery instability, at the same time. Faster and more fragile is a coherent outcome, not a contradiction — and it is the one most teams are living in without having measured it.
DORA State of DevOps research · 10 Mar 2026 · REPORTED —Published by a research programme housed at a cloud vendor.
What we actually do
The method, in detail. The reader is an engineering leader and detail is the proof.
Defect profile comparison.
Your defect data before and after AI tool adoption, segmented by severity, component and escape rate. This is the number nobody has.
Coverage mapped against AI-authored paths.
Where test coverage sits relative to the code paths that AI assistance actually touched — which is rarely where teams assume.
Targeted security review.
Weighted toward the classes where AI performs worst rather than a generic scan: injection, cross-site scripting, authentication and authorisation handling, secrets, and dependency risk.
Review process assessment.
Whether your code review process is still doing what it did before, given that 45.2% of developers report debugging AI-generated code takes longer than debugging their own.
45.2% — Stack Overflow Developer Survey 2025 · 2025 · VERIFIED
Prioritised remediation roadmap.
Ranked by risk and effort, with estimates, so it is a plan rather than a list of complaints.
What you get
- A written findings report — evidence-linked, no unsupported assertions
- A risk-ranked remediation roadmap with effort estimates
- A 90-minute walkthrough with your engineering leadership, live, questions welcome
- A fixed-price quote for the remediation build, if you want one. No obligation, no pressure, no expiring discount.
What we need from you
- Read access to the repository or repositories in scope
- Access to your defect tracker, ideally covering 6+ months before AI adoption
- One hour each with the engineering lead and whoever owns the release process
- Your CI configuration
- No production data. We work in synthetic or masked data by default and will say so in the SOW.
What this is not
This is not a penetration test, a SOC 2 audit, or a staff-augmentation contract. It is not a tool we are reselling. If your problem is that you need two testers for six months, we are the wrong vendor and we will tell you that on the first call.
Scope and terms
Two weeks fixed fee
- Priced before anything starts. One fee for the whole diagnostic, agreed in writing before we open your repository. You do not carry the risk of our estimate being wrong.
- 50% on signature, 50% on delivery of the report
- Two weeks from access granted
- If we cannot get access within two weeks of signature, we reschedule rather than burn the window.
- No retainer commitment, no auto-renewal, no expiring discount.
Tell us what is breaking.
Tell us what you adopted and what has broken since. If we cannot help, we will say so.