AI built around your data and workflows
Off-the-shelf AI tools assume your business looks like everyone else's. When it does not, you spend more time working around the tool than getting value from it. We build the thing that fits instead.
Where this usually starts
Most teams come to us after trying a generic tool first. It demoed well, it handled the easy cases, and then it hit the parts of the business that make you different. The vocabulary is wrong, the data lives somewhere the tool cannot reach, or the output is close enough to look right and wrong often enough to be unusable.
That is the point where a custom build starts paying for itself. Not because custom is always better, but because the gap between "almost works" and "people rely on it daily" is usually made of things only your own data can close.
How we approach the build
We start by mapping where your data actually lives, because that is usually the constraint that decides what is possible. Clean data in one warehouse is a different project from twelve years of records spread across a CRM, a shared drive and someone's spreadsheet.
From there we pick the smallest approach that solves the problem. Sometimes that is retrieval over your documents. Sometimes it is an agent that calls your existing APIs. Sometimes it is a plain classifier that costs a fraction of an LLM call and is more accurate for that specific job. We are not attached to any one of those.
Evaluation goes in from the first week, not before launch. You get a test set drawn from your real cases and a score you can watch over time, so when someone asks whether the AI is getting better or worse, there is an answer instead of an opinion.
What running it looks like afterwards
AI systems drift. Your data changes, your users find inputs you never tested, and model providers update the models underneath you. We build in monitoring for that from the start and hand over the runbooks, so your team can see quality and cost without calling us.
