USE CASES
Real problems.
Real results.
Your SMEs already understand 80% of the matching work in your data backlog. Coherany lets them run it themselves. An LLM coach helps them define each pattern; the deterministic engine runs every decision in production, audit-ready by default.
Same architecture. Different data. Every outcome below comes from the PAR pipeline.
Route and triage claims automatically
THE PROBLEM
Your examiners review every claim manually. At 50,000 claims per month, that's expensive, slow, and inconsistent. Per-record LLM classification costs scale to $62K/month.
THE SOLUTION
Your underwriters and claims handlers already understand the routing and fraud patterns. Coherany lets them run it themselves: cluster historical claims to surface candidate patterns, approve each one, and every new claim matches against approved patterns automatically.
You don't need to hire a data scientist. Your underwriters know the patterns. Coherany gives them the tool to run it themselves.
Categorize documents without building a taxonomy
THE PROBLEM
Clinical documents, contracts, and compliance filings need categorization. Building a taxonomy takes months. Maintaining it is full-time work.
THE SOLUTION
Your analysts already know what the categories should be. Coherany surfaces candidate categories from the data; your team approves what holds up. No predefined taxonomy. No data science queue.
You don't need to hire a data scientist. Your analysts know the patterns. Coherany gives them the tool to run it themselves.
Audit every AI decision
THE PROBLEM
Regulators want to know why every decision was made. LLMs can't explain their outputs. Your compliance team spends weeks on audit prep.
THE SOLUTION
Zero LLM calls in the production path. Every decision traces to an approved pattern, a named reviewer, and a timestamp. Audit prep goes from weeks to minutes.
Defensible to regulators by design. Every decision has a named approver and a complete audit trail.