AI integration services for software you already run.
Most companies do not need a new system. They need the one they already have to read, classify, summarise or draft — inside the screens their staff already know.
Integration is the unglamorous half of that: getting the data to the model, getting the answer back to the right place, and proving it is right often enough to leave running.
What integration actually involves
Four pieces of work, in roughly this order of difficulty.
- Models in existing software
- A feature inside your product, your admin tool or your internal dashboard. Same login, same permissions, same screens, so there is no second system for anyone to learn or forget.
- Data pipelines
- Getting the model the context it needs: documents chunked and indexed, records joined across systems, freshness handled so an answer is never quietly built on last month's data.
- Evaluation
- A test set drawn from your real cases, scored on something specific. It runs before every release and after every model change, and it is what tells you a change was an improvement rather than a different set of mistakes.
- Guardrails and cost controls
- Checks on what goes in and what comes back, a spending cap per feature, caching for repeated questions, and a smaller model wherever the evaluation says a smaller model holds.
How it works
- 1
Pick the case
One workflow, with a clear right answer and someone on your side who can tell us when it is wrong.
- 2
Build the test set
Real examples with expected outcomes, assembled before any model is wired in. This is the step most projects skip and later regret.
- 3
Wire it in
The pipeline, the model call, the fallback when a provider is down, and the place in your interface where the answer lands.
- 4
Measure, then ship
The feature launches behind a flag once it clears the test set. Cost per call, latency and failure rate are on a dashboard from the first day.
What you are left holding
The code is yours, in your repository, running in your accounts, against keys you hold. There is no runtime of ours in the path and no licence to keep paying for the right to use what we built.
We are model-agnostic by construction. The provider sits behind an interface, the evaluation runs against whichever one you point it at, and switching costs an afternoon rather than a rewrite. That matters more than it sounds: the best model for a given job changes more often than anyone would like.
The same standard covers everything we build — the three systems are on our services page.
Common questions
Do we have to replace our current software?
No. The usual shape is a feature added to what you run now. Replacing a working system in order to add one capability is how a three-week project becomes a year.
How do you stop it from making things up?
By narrowing what it is asked to do and checking what comes back. Answers are grounded in your own documents and records, the output is validated against a schema before anything acts on it, and the evaluation set catches drift before a release rather than after.
What does it cost to run the models?
That depends on volume and on which model the evaluation says you need, so we instrument it rather than guess. Cost per call is on the dashboard from the first day, with a cap per feature that stops the feature rather than surprising you at the end of the month.
Bring the workflow that would benefit most.
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