AI Agents & Automation

A model asked about your business from general training data will invent plausible answers. The fix is retrieval: the system finds relevant passages in your actual documentation, policies or product data, and the model answers from those passages while citing them. That turns an unverifiable assertion into something a person can check.

Doing this well is mostly unglamorous data work — chunking documents sensibly, keeping the index current as source material changes, and handling the case where nothing relevant is found. That last one matters most: a system that says it does not know is far more useful than one that guesses confidently.

Language model behaviour cannot be verified by clicking through it a few times. We build an evaluation set of real inputs with known-good outputs, measure against it, and re-run that evaluation whenever the prompt, the model or the retrieval layer changes. Without this, every change is a guess and regressions are discovered by customers.

We also define what the system must never do — invent policy, promise refunds, give regulated advice — and test those boundaries explicitly rather than assuming a well-worded instruction will hold.

Token costs are easy to model and easy to ignore until a batch job runs against fifty thousand records. We estimate per-task cost during scoping and design around it, using smaller models for classification and routing while reserving larger ones for work that genuinely needs the capability.

Every agent we build has a defined path to a human. Automation that traps a frustrated customer in a loop costs more in goodwill than it ever saved in handling time, so the escape hatch is designed in rather than bolted on after the first complaint.