AI Automation
LLM features and narrow agents, wired into the work you already do
We help teams put frontier models to work inside processes that already exist, invoice triage, claims intake, internal search, support deflection. Structured outputs, evals, monitoring, and a human-in-the-loop where the cost of being wrong is real. We don’t take on greenfield foundation-model training; we wire the best available models into business processes that have to run.
What we get hired to build
A few engagement shapes that come up often. Yours probably looks a bit different — that’s fine.
Document-heavy back-office workflows
Invoice categorisation, claims intake, contract triage. We build narrow agents with structured outputs, a confidence threshold, and a human-review queue for low-confidence cases. Not magic, measurable throughput in a workflow you already understand, with an audit trail your risk team can actually read.
How we engage
Three phases, with explicit decision points. You should be able to walk away after any of them with something useful.
Discovery
We start by framing the problem with the people closest to it. Technical audit of the systems you have, a written risk register for the things that worry us, and a scoped proposal you can compare against doing nothing. Fixed fee, no obligation to continue.
Pilot
A focused build behind a feature flag, kept narrow on purpose. We agree on evaluation criteria upfront, latency, accuracy, cost per run, whatever matters, and there’s an explicit go/no-go conversation at the end. If the pilot doesn’t earn its way to production, we say so.
Production
Phased rollout with monitoring, an on-call runbook, and knowledge transfer to your team. We can stay on a light retainer to evolve the system as your needs change, or hand it over cleanly. Your call.
What we work with
Tooling we reach for by default. Happy to use what your team already runs on.
Have a project that needs to actually ship?
Tell us what you’re trying to do. If we’re not the right fit, we’ll say so and point you somewhere better.
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