AI Engineering & Talent

AI Software Engineering Transformation

Coding assistants are in use whether or not they were approved, and output has risen faster than review capacity. We help engineering organizations adopt them deliberately: what is allowed, what gets reviewed, and how quality is measured rather than assumed.

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The business problem

Velocity went up and nobody checked what else changed

More code is being produced, and the review, testing and architectural oversight that used to constrain it have not scaled. The visible result is throughput. The less visible results are code nobody fully understands, dependencies pulled in without evaluation, tests generated against the implementation rather than the requirement, and a slow erosion of the shared understanding that lets a team change a system safely.

What we do

Set the policy and instrument the outcome

We establish what assistance is permitted where, including the places it should not be used, such as code touching regulated logic or third-party licensed material. Then we strengthen the constraints that now carry more load: review standards, test strategy, dependency policy and provenance recording. Most importantly we instrument the outcome, so the effect on defect rate, review latency and change failure rate is measured rather than debated.

AI Software Engineering Transformation

Capabilities

AI-Native Software Development
Coding Agent Strategy
AI-Assisted SDLC
Automated Testing Strategy
AI Code Review
DevOps Automation
Engineering Productivity

Common use cases

Establish an engineering policy for AI coding assistance that developers will actually follow.
Measure whether assisted development is improving or degrading delivery outcomes.
Bring shadow adoption of coding tools under a sanctioned, governed path.
Address licence and provenance exposure from AI-generated code before an audit or a transaction.

How we deliver

Scope

Agree the role, the stack, the seniority and how success will be judged.
Select

You interview. Nobody joins the team without your agreement.
Embed

They work in your tools, your process and your review cycle, reporting to your lead.
Sustain

Capacity flexes with the roadmap; knowledge stays documented rather than in one head.

Technology

TypeScript
Python
Go
React
Node.js
Kubernetes
GitHub Actions
Terraform

Security & governance

Engineers work under your access model and your code of conduct, on your infrastructure, with the same review and approval gates as your own staff. Intellectual property in the work is yours. Where AI coding assistance is used, it passes through the same review as anything else, and the provenance of generated code is recorded.

Engagement models

Forward-Deployed AI Team

A cross-functional AI team works inside your organization to continuously find and deliver opportunities.

Dedicated AI Team

Long-term dedicated engineering capacity built around your stack and delivery model.

Managed AI

We operate, monitor and continuously improve production AI systems.

Why TeamExtension.ai

We use these tools in production ourselves

We deliver software with AI assistance under review, on client codebases, under client standards. That means the guidance comes from practice rather than from policy writing, including the honest parts about where it does not help and where it quietly creates work.

Selected clients

Frequently asked questions

Should we ban these tools?

Bans do not hold. Developers route around them, which converts a governance problem into an invisible one. A sanctioned path with clear boundaries produces better outcomes than a prohibition nobody enforces.

How do we measure whether it is working?

Through delivery outcomes rather than activity: change failure rate, defect escape rate, review latency and lead time. Lines of code and acceptance rate measure usage, not value, and optimising for them is actively harmful.

What about licence exposure from generated code?

It is real and it is manageable. Recording provenance, scanning for known snippets and setting policy on which tools may be used in which repositories addresses most of it. The exposure typically surfaces during due diligence, which is the worst time to discover it.

Will this slow our teams down?

Some of it will, deliberately. Review capacity is the constraint, and the alternative to spending time there is spending more time later. In practice the policy work removes more friction than it adds, because uncertainty about what is allowed is itself a drag.

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Discuss Your AI Initiative

Adopt coding agents and AI-assisted delivery without losing review capacity, quality or auditability.

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