The AI transformation partner that runs its own delivery on it
AI transformation services for teams that need it working in production, not in a demo: built on our own delivery first.
What we take on in AI
An AI layer over the system you already run
The most common shape, and the one that survives contact with a business: the existing product stays, and a retrieval and reasoning layer reads what it already knows. Our largest example sits between practice leadership and a medical record, finding what the record was never queried for.
Retrieval over your own material, not a model's memory
Where answers have to come from the organisation's documents and be traceable back to them. On one product this drives personalised patient explanations, generated as narrated video from text a clinician wrote.
Applying it to engineering itself
Our audit agents — security, code review, test coverage, documentation, performance — read client repositories on a weekly schedule. Since March 2026 they have run 1,699 times across 11 client projects. That is AI doing unglamorous work on a schedule, including the part nobody demos: 144 of those runs failed, and the diligence page says why.
Telling you when not to
A retrieval pipeline over documents nobody maintains returns confident nonsense faster than a human could. Where the answer is a database query, a form, or fixing the data, we will say so — and on several engagements that has been the whole recommendation.
Enterprise AI transformation services
The review committee is the real deadline
Enterprise buyers now run formal AI review committees, and one of our healthcare clients has one. Its verdict, not the build, decides when the project ships. So we build for it from the first week: an evaluation set the business agrees with, results you can show the committee rather than describe, boundaries a compliance officer accepts, and a clear list of what the system refuses to answer.
Inside the system of record, not beside it
Enterprise AI earns its keep where the data already lives. Our largest engagement is an intelligence layer between practice leadership and a medical record: the record stays as it is, and the AI reads what it already holds to surface what nobody had queried it for. That shape survives procurement, security review and the team that has to maintain it, where a parallel tool with its own copy of the data usually does not.
Owned after launch, with the bill visible
Monitoring, cost per call, what happens when the model changes underneath you, and who is accountable when an answer is wrong are agreed before go-live, not after the first incident. The hours are logged and published the way they are across this site, and the team scales down without renegotiating.
How an AI engagement starts
- Days 1–2
What decision is this meant to change, and what does the organisation already have written down. Most AI briefs that fail were problem statements that would have failed as a report.
- Weeks 1–2
The narrow version first: one workflow, real data, an evaluation set someone in the business agrees with. If it does not clear that bar, stopping here is the cheap outcome and we will recommend it.
- After it works
The part nobody demos — monitoring, cost per call, what happens when the model changes underneath you, and who is on the hook when an answer is wrong.
- Every month after
The hours logged, published the way they are across this site, and a team you can scale down without renegotiating.
Working with a AI team
Where the team sits
More than half our engineers now sit in Latin America, with most of the rest in Eastern Europe. On AI work the overlap matters for the evaluation loop: somebody from the business has to look at outputs and say whether they are right, and that conversation happening the same day is the difference between a two-week iteration and a two-month one.
How people get here
Ten of our engineers hold the Claude Certified Architect – Foundations certification, and AI tools are part of the standard setup on every engineering team. Hiring runs through the same funnel as every other stack — roughly one hire per 130 applications on the front end and 157 on the back end.
Our Awards
Frequently Asked Questions
How much AI work have you actually shipped?
What does your own team actually use?
Did it change anything you can measure?
Has any of this actually changed how fast work moves?
What does an AI engagement cost before it earns anything?
When should we not do this?
