AI development and integration

Intelligence built in, not bolted on

Most AI work that fails does so in the same way: a model is impressive in a demo and useless in a product, because nothing around it was designed. The model was the easy part. What decides whether an AI feature works is everything else — what context reaches it, what it is allowed to do, what happens when it is wrong, and how a user recovers from that.

We build AI as a feature of a working product, subject to the same standards as every other feature. It has to be correct often enough to be trusted, cheap enough to run at your actual volume, and fast enough that nobody abandons the page waiting for it.

LLM integration

Putting a large language model into an existing product is mostly a data problem. The quality of an answer is set by the quality of the context it was given, which means the real work is retrieval: finding the right documents, records or history, and getting them into the prompt in a form the model can use.

That covers retrieval-augmented generation over your own content, structured extraction that turns documents and free text into records you can query, classification and routing, summarisation of long material, and natural-language interfaces over data that previously needed a form.

AI agents and tool use

An agent is a model given tools and permission to use them in sequence. That is powerful and it is exactly where things go wrong, because an agent that can act can also act incorrectly, repeatedly, at speed.

So we build them with the boundaries first: an explicit set of tools rather than open-ended access, validation on everything a model returns before it reaches a system that matters, limits on how long a loop may run and what it may spend, and a human confirmation step in front of any action that is hard to reverse. Every run is logged and inspectable, because an agent you cannot audit is one you cannot fix.

Choosing a model honestly

Model choice is a cost and latency decision far more often than a capability one. A large frontier model is the right answer for hard reasoning and the wrong answer for classifying ten thousand short strings, where a smaller model does the same job for a fraction of the price and a tenth of the wait.

We measure this against your workload instead of guessing, and we build so the model can be swapped. This field changes fast enough that any system welded to one provider API is already carrying a rewrite it has not paid for yet.

Where to start

The best first AI feature is usually something narrow and repetitive that a person currently does by hand — reading documents, sorting requests, pulling numbers out of text. It is measurable, it is contained, and if it works you know quickly.

Tell us what that task is in your business and we will tell you whether it is a good candidate, roughly what it would cost to run, and what it would take to build.