Human judgment, amplified.
Practical ways AI can help adjusters and investigators organize information, identify gaps, structure better questions, and improve workflow—without outsourcing professional judgment.
Use AI where it improves the work.
The strongest use cases reduce administrative drag or improve analytical structure while keeping the professional responsible for facts, judgment, and the final decision.
Claim chronology
Organize dated events, identify gaps, and surface conflicting timelines for human review.
Referral development
Turn a claim issue into clearer investigative objectives, questions, known facts, and information gaps.
Interview planning
Build topic outlines, sequencing, follow-up questions, and corroboration targets before an interview.
Research organization
Structure lawful OSINT findings, timelines, relationships, sources, and unresolved leads.
Report review
Compare the investigative objective to the reported work and identify unanswered questions or unsupported conclusions.
Training & quality
Create scenarios, coaching frameworks, QA checklists, and consistent learning materials—with human review.
AI assists. The professional decides.
Claims and investigation work can involve sensitive data and consequential decisions. Responsible use requires privacy discipline, verification, governance, and clear accountability.
NAIC Model Bulletin & regulatory work
NAIC guidance emphasizes governance, risk management, applicable insurance law, and regulator visibility into insurer use of AI-supported decisions.
NAIC Artificial Intelligence resources ↗NIST AI RMF
NIST's voluntary framework is designed to help organizations govern, map, measure, and manage AI risks, including generative-AI-specific considerations.
NIST AI Risk Management Framework ↗From guidance to usable tools.
Our roadmap is focused on privacy-conscious, vendor-neutral tools that help insurance professionals structure the next step—not automate the decision.
View the Labs roadmap →