A chatbot was added last quarter. It answers confidently and sometimes wrongly, the prompts live in a file nobody dares touch, and the monthly bill was a surprise.
We treat a language model like any feature that can fail. The data is cleaned before retrieval, an evaluation set exists before the first prompt, answers cite their source and hand over to a person when the stakes are high. The model sits behind an interface, so changing providers is a configuration change.
What we offer
- Answers from your own documents
- Evaluation sets and prompt tests
- Guardrails and human escalation
- Provider-agnostic, with a cost budget
How we work
- 01Pick the feature, define a good answer
- 02Structure the source data
- 03Build the evaluation set first
- 04Build, measure, tighten
- 05Ship behind a flag, watch cost and quality
Offers
- AI Sprint: One process, two weeks, a measured result.
Also in AI
- AI Strategy & Readiness: Which use cases pay, in what order, and what is not worth it.
- AI Automation & Agents: Repetitive work done by software, with people where it matters.
- Data & Custom ML: Trained models and optimisers for the problems a prompt cannot solve.
Ready to talk?
