AI systems for knowledge-heavy operations teams
Bring the policy, the record, and the precedent to the operator in one place, so two capable people reach the same decision.
When an experienced person leaves, the part of the job that lived only in their judgement leaves with them.
the decision that takes four tabs
An operator opens the case, then the policy document, then the record in another system, then a spreadsheet somebody maintains privately, and forms a judgement from all four. Two capable people doing that reach different conclusions, and neither can fully explain the difference. The work is not slow because people are slow. It is slow because the evidence never arrives assembled, so every case pays the assembly cost again. New starters feel this hardest: they can read every policy and still not know which one wins.
the workflows we take on here
- Assembling the evidence for a case before an operator ever opens it
- Checking an application against the policy that actually governs it
- Drafting the decision letter once a person has made the decision
- Surfacing the precedent set the last time a case looked like this one
what changes when the answer arrives with its evidence
Four systems, a private spreadsheet, and a judgement whose reasoning disappears the moment the case is closed.
The relevant policy, the record, and the precedent presented together, with the operator’s reasoning captured as part of the case.
where exceptions must escalate
- Cases where the policy is genuinely silent, rather than merely hard to find
- Anything where two authoritative sources disagree and somebody must choose
- Decisions that set a precedent the team has not agreed to yet
- Any case a customer has already disputed once
the judgement call we will not take from you
We will not encode a policy your team has not settled. Automation makes an inconsistent rule inconsistent much faster, and the disagreement then becomes a software problem instead of a management one.
Thirty minutes, no charge, no deck required.