AI Systems

Models pointed at one job, with a number attached.

Language models applied to a specific task with a measurable success rate — not a chatbot bolted onto a homepage. Retrieval over your own documents, extraction from messy inputs, classification and routing, and agents that take real actions against real systems.

We start by finding the judgement call. A model is only worth introducing where a person is currently making a decision that is repetitive and rule-bounded — everything else is better served by ordinary code, and we will tell you when that is the case.

Then we measure. Before anything reaches production we build a held-out set from your real data and score against it, so accuracy is a number you can see rather than a feeling. That set becomes the regression test for every later change.

Finally we gate it. Every system we ship can stop and ask a human when confidence is low, and those corrections flow back into the evaluation set — so the same doubt costs less to resolve the second time.

You might need this if…

1. Someone re-types data from PDFs or emails into a system every day

2. Support answers the same twenty questions in slightly different words

3. Institutional knowledge lives in documents nobody can search

4. You tried a chatbot and quietly turned it off

What you end up with

Outcome

A measured accuracy figure on your own data, not a vendor benchmark

Outcome

A human-review queue for anything below threshold

Outcome

Cost per run, monitored and tuned

Outcome

Documentation your team can maintain without us

• Retrieval pipelines
• Document extraction
• Support & triage agents
• Evaluation harnesses
• Model & cost tuning

The other practices