What's blocking your insurance AI team?
Your underwriting and claims AI look like they're working. You don't know what they're getting wrong.
Surface failures in production traces before they reach customers or regulators, including wrong decisions, missed fraud patterns, and outputs that would fail underwriter or actuary review.
Underwriters and claims specialists need to review AI decisions, but reviews happen in email and spreadsheets.
Route specific cases to underwriters and claims experts with the context they need. Reviews happen through a structured workflow with shared rubrics, and every judgment gets recorded.
State insurance regulators are asking for documentation of how AI decisions are validated and reviewed. Most teams don't have it.
Every failure found, review completed, and fix validated is recorded in a single audit-ready trail. When regulators ask, the answer is already documented.
What DataFramer does for insurance AI teams
Find what your models are getting wrong
Surface failures in underwriting, claims automation, and fraud detection that standard metrics miss: decisions that look correct until an underwriter or adjuster challenges them.
Know where in the workflow it broke
When a failure surfaces, narrow down whether it came from the model, the features, the business rules, or the upstream data. In complex decisioning systems, the failure is often several steps back from where it shows up.
Structure expert review with underwriters and actuaries
Route specific cases to underwriters, claims specialists, or actuaries with the context they need. Reviews happen through a structured workflow with shared rubrics, and every judgment gets recorded.
Calibrate automated scoring against expert judgment
Automated claim assessments or fraud scores need to reflect what your underwriters and claims teams actually consider acceptable. DataFramer uses reviewed examples to keep automated scoring aligned with real expert judgment.
Validate model changes before they ship
When a model or business rule changes, test it against decisions that have already been reviewed by underwriters. State regulators increasingly require proof that new versions don't break prior validated behavior.
Use cases
Underwriting Decision Review
Find when your underwriting model starts making decisions outside normal patterns or contradicting underwriter judgment
Claims Automation Quality
Route complex or borderline claims to specialist reviewers and document the outcome for consistency and compliance
Fraud Detection Monitoring
Surface patterns your fraud model is missing before they become claim losses
Rate and Premium Validation
Ensure pricing models produce decisions that are consistent with underwriting guidelines and fair across customer segments
Customer Complaint Investigation
When a customer disputes a decision, trace the AI reasoning and document the expert review that followed
Cross-Product Quality Monitoring
Track failure patterns and quality scores across underwriting, claims, and fraud models in one place
Find what your insurance AI is getting wrong.
Connect your production traces and see the decisions your metrics are missing.
Common questions from insurance teams
How does DataFramer help with state insurance regulator requirements?
State insurance bulletins on AI are increasingly requiring documented model governance, expert review, and an audit trail of decisions. DataFramer builds that trail automatically: failure discovery, expert review with underwriters and actuaries, and validated fixes all recorded in one place.
Can underwriters and claims specialists use DataFramer without being data scientists?
Yes. Underwriters and claims experts get specific cases with shared context and clear questions to answer. They don't need to understand the model internals. Their judgment gets recorded in a form that flows directly into the model improvement process.
What insurance AI systems does DataFramer work with?
DataFramer works with underwriting decisioning systems, claims automation, fraud detection, pricing models, and any other insurance AI that produces traces. It integrates with LangFuse, LangSmith, and other observability tools.
How does DataFramer create an audit trail for regulators?
Every failure found, review completed, decision made, and fix validated is recorded in DataFramer. When state regulators ask for documentation of your model oversight process, the record is already there.
Does DataFramer deploy in our own environment for sensitive underwriting data?
Yes. DataFramer deploys inside your own cloud or on-prem infrastructure. Production traces and model outputs stay within your governance boundary.
How does DataFramer help with fairness and non-discrimination in underwriting?
DataFramer helps teams find AI decisions that look borderline or inconsistent, route them to expert underwriter review, and maintain a record of how each case was reviewed and why it was approved or declined. That documentation supports fairness and non-discrimination requirements.