Dedicated AI Teams
A standing squad that stays long enough to learn your business. AI engineers, application engineers, evaluation and MLOps, working only on your work, for a programme rather than a pilot. The most expensive shape we offer and occasionally the only one that fits.
You set direction. We run the delivery.
With a dedicated team most of the delivery moves to us, which is what you are paying for. Direction does not, and should not.
A team is a commitment, in both directions.
The argument for a dedicated AI team is accumulated context: what your data actually means, which edge cases matter, what a good answer looks like in your business. None of that fits in a prompt and none of it survives a handover. The argument against is that you pay for it from week one, including the weeks when there is less to do.
The lead is picked first
A dedicated team is only as good as whoever holds its standards. We choose that person before anyone else and you interview them like a hire.
Evaluation sits inside the team
There is no separate QA phase for a model. Whoever builds the feature builds the eval set that proves it works, in the same week, on your real cases. That is most of the quality difference.
The team stays together
Rotating people through defeats the purpose, because the thing you are paying to accumulate lives in their heads. Churn on our side is our problem to absorb, not yours to re-onboard.
Scaled down without drama
If the programme shrinks, so does the team, on 30 days notice. We would rather resize than quietly bill for idle seats.
Three ways to work with us. This is one.
Same bench, different shape. Pick the wrong one and you pay for coordination you did not need.
A team is often more than the problem needs.
Good fit
- A programme of AI work with twelve months of visible roadmap.
- Several workstreams that would otherwise contend for one engineer.
- You need evaluation and MLOps but not enough to hire for them.
- Understanding your data takes months and you keep losing that knowledge.
Poor fit
- One well-defined feature. Augmentation or AI and LLM integration fits better.
- The roadmap is uncertain. You will pay for idle capacity.
- You have not got someone to set direction for them.
Before you book the call.
Yes, and we usually recommend it. Two or three people for the first six weeks, then scale once the roadmap proves itself. Starting at eight is how teams end up with people looking for work to justify themselves.
Yes. That is the definition, and it is what separates this from augmentation with extra steps. Nobody on a dedicated team is split across accounts.
No, and a team that was would build you something impressive that nobody can run. Most AI work is ordinary software engineering around a model: data plumbing, interfaces, permissions, monitoring. We staff for that ratio honestly and tell you what it is before you sign.
Yours throughout, in your repository, including the prompts, the eval sets and the reasoning behind both. A team that leaves without leaving its evaluation behind has not finished.
Tell us the programme, not the headcount.
Thirty minutes. If a team is more than you need, we will say so and propose the smaller shape.