Volume grows faster than you can hire reviewers.
ENTERPRISES / SOLUTION
Move governed AI into production.
A review queue is a place where trained people apply rules to a stream of items. Agents can carry most of that load, if every decision arrives with the evidence behind it.
The problem today
Consistency depends on which person picked up the item.
When a regulator asks why a decision was made, the answer lives in someone's memory.
What the agent does
Retrieve
Pull the candidate matches from the sources that govern the decision.
Rank
Score and reorder them so the strongest evidence surfaces first.
Apply rules
Run the deterministic checks that are not open to interpretation.
Present for approval
A reviewer gets a recommendation, the evidence, and a one-click decision that is recorded.
What it touches
- Case system
- Policy sources
- Documents
- Identity
- Audit log
- Reporting
FAQ
Citations to the source that drove the decision, deterministic rules kept separate from model judgment, a human approval step on anything consequential, and an audit trail that reconstructs why an item was handled the way it was.
Precision and recall against a labeled set, reviewer throughput against the manual baseline, false-positive rate, and how often a reviewer overturns the recommendation. Targets get agreed before the build, not after.
No. Retrieval, ranking, rules, and language work are separate jobs with separate tools. A single model asked to do all of it is harder to test and harder to defend.
The final call on anything with consequences, the edge cases the system flags as uncertain, and the judgment about whether the rules themselves still make sense.
