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AI agents. Scoped to one job, and measured on it.

Agents that do real work against your systems. Not a chatbot bolted to the corner of a page.

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An agent without evaluation is a demo

Most AI pilots fail for the same two reasons: the scope was "help with things" rather than a specific job, and nobody defined what a correct answer looks like, so nobody could tell whether it was working.

We build agents the other way round. One workflow with a measurable outcome, connected to the systems where your data actually lives, with an evaluation set written before the agent is built.

Useful shapes we see repeatedly: triaging and routing inbound enquiries, drafting first-pass content against a documented brand voice, extracting structured data from documents, and answering internal questions from your own knowledge base. The wider automation layer lives in process automation.

How we actually do it

Narrow, evaluated, monitored.

01 · Pick one workflow with a measurable outcome: Volume, cost or time saved, something you can count before and after. Broad assistants are impossible to justify at renewal because nobody can say what changed.
02 · Write the evaluation set before the agent: A reference set of realistic inputs and acceptable outputs, including the awkward edge cases. This is what turns "it seems good" into a number, and it is the step almost everyone skips.
03 · Connect it to real systems: An agent that cannot read your CRM or write to your CMS is a toy. We build the integrations and the guardrails, what it may do autonomously and where a human has to approve.
04 · Monitor it in production: Model behaviour drifts and inputs change. We log, sample and re-evaluate on a cadence so quality is observed rather than assumed.

What you get

Deliverables, not adjectives.

A scoped workflow with a defined, countable success metric
A reference evaluation set, versioned and re-runnable
The agent, integrated with the systems it needs to read and write
Guardrails and human-approval points where the risk warrants them
Production logging, sampling and a re-evaluation cadence

Questions, answered straight

No fluff, as promised.

Which models do you build on?

Whichever fits the job, usually the current Claude or GPT models, chosen on quality, latency and cost for your specific task rather than on preference. We benchmark rather than assume.

How do we stop it inventing things?

Constrain it to your data through retrieval, make it cite what it used, evaluate against a reference set, and keep a human approval step wherever a wrong answer is expensive. Guardrails are design decisions, not settings.