Accelerate your AI transformation with Generative AI Automation
Improve your business exponentially with the right AI workflows.
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 tool reads repositories and scores them across ten dimensions; it ran 376 times across 120 repositories between September 2025 and January 2026, and 250 of those runs produced a report with findings in it. That is AI doing unglamorous work on a schedule — including the part nobody demos, which is that 126 of the 376 runs failed quietly and the diligence page says so.
Evaluation, and the committee that signs it off
Enterprise buyers now run formal AI review committees — one of our healthcare clients has one, and its verdict is the real deadline on the project. Building for that means evaluation you can show, boundaries a compliance officer accepts, and knowing what the system refuses to answer.
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.
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 all 32 people here use AI tools weekly. 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.
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?
