Correctness changes with context
The right behavior depends on risk and customer context, policy and jurisdiction, evidence sufficiency, exceptions and escalation. Generic scoring misses those interactions.
Solutions · Insurance
Insurance AI that respects context, evidence, and policy intent
Make underwriting, claims, and service judgment testable across real-world exceptions.
Insurance work turns on context: policy language, evidence quality, jurisdiction, customer circumstances, and exceptions. Teammately captures that judgment as reusable benchmark infrastructure.
The domain reality
Generic quality is not enough for consequential insurance behavior.
The standard lives across specialist teams
Underwriting appetite, Claims interpretation, Customer communication, Regulatory and conduct policy each hold part of the judgment the system needs to behave well.
Real work is full of exceptions
A benchmark must represent edge cases, uncertainty, conflicting goals, and escalation—not only the most common insurance path.
Priority workflows
Start where expert judgment materially changes the answer.
Underwriting copilots
Evaluate evidence use, risk reasoning, appetite boundaries, and referral decisions.
Claims assistants
Test policy interpretation, missing evidence, customer explanation, and escalation.
Service agents
Benchmark accurate, empathetic communication across coverage and claims scenarios.
Quality review
Apply specialist rubrics consistently while preserving the evidence behind every result.
Correctness blueprint
Connect specialist judgment to the benchmark and the build.
Your experts define
- Underwriting appetite
- Claims interpretation
- Customer communication
- Regulatory and conduct policy
The benchmark covers
- Risk and customer context
- Policy and jurisdiction
- Evidence sufficiency
- Exceptions and escalation
Your AI team receives
- A deliberate coverage map
- Explicit policies and binary rubrics
- Targeted cases, variants, and exceptions
- Inspectable evaluation and improvement evidence
One connected workflow
Five product capabilities, applied to one domain standard.
Coverage Engineering
Design the combinations of risk and customer context, policy and jurisdiction, evidence sufficiency, exceptions and escalation the benchmark must represent.
Correctness Elicitation
Turn judgment from underwriting appetite, claims interpretation, customer communication, regulatory and conduct policy into policies, applicability conditions, and binary rubrics.
Weave
Create targeted insurance cases, variants, artifacts, and worlds from the coverage plan.
Trialground
Run candidate models and agents in controlled environments and preserve the behavior-level evidence.
Coevolve
Explore parallel improvement directions and return newly discovered gaps to the right specialists.
What good looks like
A development team that can improve behavior without losing domain intent.
Coverage you can defend
Know which insurance conditions, exceptions, and risks the benchmark represents—and which it does not.
Judgment that scales
Reuse every specialist decision across policies, rubrics, evaluation, and future cases.
Evidence for every iteration
Compare model, prompt, harness, and agent changes against the same domain-grounded standard.
A controlled learning loop
Route unresolved questions and newly discovered gaps back to accountable experts.
Teammately helps teams test and improve insurance AI; accountable underwriting, claims, and regulatory decisions remain human-owned.
Build a benchmark around how your insurance experts actually judge quality.
Start with a consequential workflow and the specialists already accountable for it. Teammately turns their judgment into reusable development infrastructure.