Governing AI and human actions across the tools your teams already use
- Claude
- ChatGPT
- Slack
- Microsoft Teams
- Microsoft 365
- Gmail
- GitHub
- GitLab
What it does
- Regulatory Response — Continuous, cited evidence for NAIC, NYDFS, EU AI Act, Solvency II and IRDAI.
- Evidence — Reconstruct any underwriting or claims decision, with the inputs, the rule applied and the named owner.
- One rule set — Underwriting, claims and compliance run against one policy rather than three interpretations of it.
What gets in the way
Regulatory scrutiny on automated underwriting
Regulators are examining how technology — from legacy rule engines to AI models — influences pricing, risk selection, and policyholder outcomes. You need auditable evidence for every decision.
Claims automation opacity
Automated claims decisions are challenged in courts and regulatory exams. Without forensic traceability, you can't defend what your AI did or why.
Inability to insure technology decisions
Carriers want to underwrite technology risk but lack the runtime evidence to price it. The data layer between technology operations and insurance doesn't exist — until now.
Siloed decision intelligence
Learnings from underwriting don't reach claims. Insights from claims don't improve pricing. Your systems operate in isolation instead of compounding intelligence.
Start with one service.
A 30-minute scoping session with your risk owner and your IT contact.