Document formats change, carriers change their templates, models get deprecated. Something has to notice.
What you get
The test set is re-run on a schedule and after every change. Accuracy is a tracked number over time, not a claim made once at launch.
Alerting when input documents start looking different from what the system was built on — a carrier redesigning a loss run is the kind of thing that quietly breaks extraction.
New model versions evaluated against your test set before adoption, so an upgrade is a measured decision rather than a hope.
The exceptions your team routes to a human get folded back in, so the system covers more over time instead of plateauing.
An agreed SLA for when something breaks, and a named engineer who already knows the system.
Fit
Questions
It is closer to operations than support. Most of the work is measurement and adaptation — re-running evals, catching drift, absorbing new document formats — rather than waiting for a ticket.
You can, though the useful moment is at handover while the system is fresh and the test set is current. Picking it up a year later usually starts with rebuilding the measurement that lapsed.
Scoped at handover against volume, integration count, and the response time you need. It is a monthly commitment, not a block of hours that expire.
The ladder
01 — $2,500
You should not have to sign a six-figure contract to find out whether an AI agent can read your submissions. Spend a week and a fixed fee instead.
02 — Scoped
Most agent projects stall because nobody can prove the thing is right. We ship the proof alongside the system.
Next step
A week, a fixed fee, and a measured answer on your own documents. If it will not work, you find out for $2,500.