The job comes first.
Two weeks from "we should use AI" to a costed, sequenced plan — which workflow, which model, and what it costs to run.
Most AI products fail at the last mile — the interface. That's where we live.
The model is the easy part now — you rent it by the token. What earns trust is the product around it: how it answers, when it admits doubt, and who it hands the hard cases to.
Four steps, start to finish.
Connect your sources
Point it at the documents, tickets and records your team already answers from. Nothing is copied anywhere new.
Shape the answers
Set the voice, the escalation rules and the cases it should never attempt on its own.
Watch it work
Every answer arrives with its sources and a confidence reading, so your team can see why it said what it said.
Hand over the rest
Low-confidence cases route to a person with the full context attached, not a blank ticket.
From question to running system.
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01
Discover
Two weeks inside the workflow: what it costs today, where it breaks, and which parts a model can genuinely carry.
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02
Shape
A costed plan with the model chosen, the interface sketched and the success measure agreed before anything is built.
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03
Build
Weekly releases against real traffic, with evaluation sets that grow as the edge cases surface.
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04
Hand over
Your team ships the next change. We leave the runbook, the evaluations and a month of support behind us.
What they say after.
They shipped a working assistant in six weeks, then spent the seventh teaching our team to run it without them.
The audit paid for itself twice over. Half of what we planned to build turned out not to need a model at all.
What we got was a product our support team actually opens, not a demo that impressed the board once.
Have a workflow in mind?
Two weeks from question to costed plan. Tell us the job to be done and we will tell you whether a model is the right tool for it.