AI-Assisted Engineering
Coding agents doing the routine work, with your people still deciding what gets built and what ships.
We run this on our own products first
Most teams have tried a coding assistant and found it useful for small things and unreliable for real work. The gap is rarely the model. It is that the agent has no idea how your codebase is meant to be written, and nobody has decided which jobs it should be given.
We set that up: the working practices, the documentation an agent can actually follow, and the split between what your people decide and what the agent does. Then we hand it over.
Here the agent works on your codebase. If what you want is an agent running an operational process instead - triage, document handling, reconciliation - that's Agentic AI Development.
What we set up
Plan expensive, build cheap
The hard thinking goes to the most capable model; the implementation that follows a good plan does not need one.
Documentation an agent can follow
Conventions, house rules and every mistake worth not repeating, kept in the repository where the agent reads them.
Review as the control point
Your people plan the work and review what comes back. Nothing merges without an experienced developer.
Tests the agent has to pass
Automated tests on every change make this safe. Runs, coverage and quality feed a dashboard that summarises each release.
Over 1,000 tickets, none of the code written by hand
This is how we build our own products, and the numbers below are ours rather than a client's.
| Item | Result | Notes |
|---|---|---|
| Human-written code | None | on one of our own products: every change planned by our team, implemented by a coding agent working to the conventions we keep in the repository, and reviewed by qualified and experienced developers before it merged |
| Tickets closed this way | 1,000+ | as of September 2026 |
| On a second product | 200+ | now being built the same way |
What your team keeps
Setting up the tools is the fast part. The slower work is a team that still uses them well a year on, so the approach, the risks and the training get as much attention as the install.
Start where the friction is
We work directly with your engineers first: how they build, test and deploy today, and where the friction actually is. That decides where AI lands first and how far it goes - an approach sized to your team's maturity and delivery model. Where the pipeline is in scope, that includes AI in monitoring, incident detection and release management.
Guardrails for AI-written code
AI-generated code raises questions a human's doesn't: who owns it, whether it's secure, whether it meets your standards. We set up the review processes and quality controls that answer them, so AI-assisted work stays inside your engineering standards and your compliance obligations.
Capability that stays
We assess where your engineers are with these tools - capability and confidence both - then train to the gap: the practices most relevant to your stack, and ways of working that hold up as the tools change. When we leave, the capability doesn't.
Common questions
Does this replace our developers?
No. It moves them up a level. Your qualified, experienced developers plan the work and review what comes back; the agent does the typing. The judgement calls - architecture, trade-offs, what good looks like - still need someone who knows your business.
How do you stop an agent shipping something wrong?
The same way you stop a person doing it: tests that run on every change, and a review by a qualified, experienced developer before anything merges. An agent that can't get past your test suite can't get into production either.
What does it cost to run?
Less than most teams assume, once the work is split by difficulty. Sending every task to the most capable model is where budgets disappear. Planning needs that model; implementation, once the plan is good, often doesn't. Smaller open-weight models do much of that work at a fraction of the cost per change. They have implemented tickets that passed our tests and went into production, and they can run entirely on hardware you control.