How we work
How we work with you.
We configure the platform alongside your team, start with every result reviewed by a person, and widen what the AI does only as it earns it.
How we engage
We stay in the room.
Document AI projects rarely fail at the model. They stall in configuration, where somebody has to work out what the process really does, what ‘correct’ means at each step, and who decides when it’s unclear. That work needs your people and ours in the same room.
The usual model
A platform, an API key, and a services partner you hire separately to work out the rest.
Ours
Our team configures it with yours, then hands over something your people already know how to run.
The configuration work
Five things we build with your team.
Workflow & validations
How the process actually runs, and what counts as correct at every step.
Knowledge that scales it
Your rules and judgment, captured once so the platform applies them everywhere.
Review ergonomics
Iterative design on the review screens, because a reviewer's speed is a real cost.
Data definitions & classification
Built to survive a change of foundational model, not tuned to today’s one.
Activity plans
Where the AI runs, where a person decides, and how the two hand off.
None of this is a questionnaire. We sit with the people who do the work today, watch what they actually check, and build that.
Earning autonomy
The AI earns its autonomy, one case at a time.
Nothing runs unattended at the start. Work moves from fully reviewed to running on its own only as the evidence earns it.
Phase 1 · Validate everything
On day one, a person sees every result.
The AI does the work, your team checks all of it, and the disagreements are the point. That’s how you find out where it’s genuinely reliable and where it isn’t, on your documents rather than someone’s benchmark.
Every document, checked
100% human review. Slower than the demo, and the only honest way to start.
Phase 2 · Read the evidence
Trust comes from numbers you read yourself.
Every run leaves an audit trail and a set of measures: where the AI agreed with your reviewers, where it didn’t, and what changed after each correction. We teach your team to read them, because a claim from us is worth much less than a number they checked.
Phase 3 · Expand autonomy
The AI earns more work, one case at a time.
We grant autonomy per document type and per field, never in one switch. When the evidence holds for a category, we let that category run and keep the rest under review. Your team sets the thresholds. Anything that drops below one comes straight back to a person.
Phase 4 · The loop
Then we build the loop, and watch for the world moving.
Every correction becomes knowledge the next run starts with. But knowledge has a shelf life. A vendor changes terms, a regulator changes a form, a counterparty changes its format. We build the checks that notice when agreement starts slipping on a category, so you find out from a measure rather than from a customer.
Agreement on one category
Knowledge out of sync. This vendor’s tax calculation approach changed.
How it ends
We are trying to leave.
The engagement is fixed and deliberately short. If we can’t step back on schedule, the knowledge never really transferred, and that’s our failure rather than yours. The end date is the quality test we hold ourselves to.
The knowledge
Your rules and corrections, in your tenant, portable across any model.
The configuration
Activity plans, data definitions and review screens your team can change.
The evidence
A full audit trail and the measures to prove it still works.
What we need from you: time with the people who do this work today. That’s the one thing we can’t supply.
Start with your workflow, not our platform.
Show us the process you’d most like to stop doing by hand, and we’ll walk you through how the first weeks would run.