Trust intelligence for insurance
From policyholder concierge to fraud, waste, and abuse detection. AI that works at scale — not just in demos.
Your agentic AI POC worked. Production didn't. We can help.
The POC-to-Production gap
You proved the concept. The demo impressed leadership. Then you tried to scale it — and hit a wall.
Costs exploded
POC: $500/month for demos. Production: $30,000-50,000/month for real volume. Finance is asking questions you can't answer.
Latency killed UX
POC: "Wow, it thinks!" Production: "Why is this so slow?" Agents reasoning for 3-5 seconds per request. SLAs missed.
Reliability wasn't enterprise-grade
POC: "It works 90% of the time." Production: "90% isn't good enough." Hallucinations. Edge cases everywhere.
Ops couldn't manage it
No observability. No governance. No audit trail. When it breaks, nobody knows why.
"The POC proved agentic AI can work. Production proved you need architecture, not just agents."
Fraud, Waste & Abuse Detection — Done Right
Not "use less AI" — use AI where it matters. The Intelligent Trust Cascade routes each claim to the cheapest processing layer that can handle it.
Click any level to explore details
Deterministic rules catch known fraud patterns instantly. No AI needed for obvious cases.
- Duplicate claim submissions
- Claims exceeding policy limits
- Velocity checks (too many claims too fast)
- Known bad actor lists
- Invalid provider/procedure combinations
Traditional ML models detect statistical anomalies and patterns that rules can't express.
- Unusual billing patterns for provider type
- Geographic anomalies
- Procedure frequency outliers
- Network analysis (provider rings)
- Temporal pattern anomalies
LLM agent reasons about complex cases, pulling context from multiple sources to make a decision.
- Medical necessity evaluation
- Complex documentation review
- Multi-claim pattern analysis
- Provider behavior reasoning
- Policy interpretation edge cases
Adversarial multi-agent debate for the highest-stakes decisions. Three agents argue it out.
- Prosecutor: Argues for fraud designation, finds evidence
- Defense: Argues for legitimacy, finds counter-evidence
- Judge: Weighs arguments, makes final determination
Level 1: Rules Engine
Known patterns, velocity checks. 70% of claims are obvious — rules catch them in milliseconds at $0.0001 per claim.
Level 2: Statistical ML
Anomaly detection, risk scoring. 20% have patterns ML recognizes. Sub-second response at $0.001 per claim.
Level 3: Single Agent
Complex pattern analysis. Only 7% need agent reasoning. 2-3 second response at $0.01 per claim.
Level 4: Multi-Agent Tribunal
Full adversarial debate for high-stakes, ambiguous cases. Prosecution, defense, and judge agents argue it out. Only 3% of claims — but the ones that matter most.
Production-grade economics
Compare: Pure agentic approach costs $30,000-50,000/month for the same volume. That's the difference between a science experiment and a business case.
Production-grade operations
The cascade alone isn't enough. Production requires continuous monitoring, observability, and self-improvement.
Continuous Monitoring (Guardian)
Track detection accuracy over time. Detect model drift as fraud patterns evolve. Alert when reliability degrades. Know before customers do.
Full Observability (AgentOps)
What the cascade decided and why. Which layer handled which claims. Cost attribution by claim type. Audit trail for compliance and litigation.
Self-Improvement (APLS)
When expensive layers catch fraud that cheap layers missed, the system extracts patterns and proposes new rules. Over time, detection migrates from $0.05 to $0.0001. The system gets cheaper and better simultaneously.
Adversarial Testing (Red Queen)
Genetic algorithm continuously probes the system. Strongest "attacks" train the cascade. The system evolves against emerging fraud patterns before they become incidents.
Beyond fraud detection
AI Concierge for Policyholders
24/7 AI assistant that knows your policy, answers questions instantly, helps file claims. Guardian monitors for hallucination. Steer enforces compliance language. AgentOps provides full audit trail. Products: Guardian, Steer, AgentOps, ETL-C
AI-Assisted Underwriting
Synthesize data from dozens of sources — medical records, financial data, third-party scores. ETL-C provides contextual integration. Guardian tracks model accuracy. Full reasoning capture for explainability and adverse action documentation. Products: ETL-C, Guardian, AgentOps
Claims Automation
Trust Cascade for claims adjudication. Simple claims processed automatically. Complex claims routed to appropriate level. Full audit trail for every decision. Products: Orchestrate, Guardian, ETL-C
Built for insurance regulation
Our solutions align with regulatory requirements from day one.
NAIC Model Bulletin
Aligned with NAIC's 2023 Model Bulletin on AI in insurance.
State Insurance Departments
Compliant with state-level AI guidelines and requirements.
Fair Lending & Anti-Discrimination
Bias testing and fairness monitoring built in.
SOC 2 & Data Privacy
SOC 2 compliant. GDPR/CCPA data requirements supported.
Start your insurance AI journey
FWA Assessment
$30K
2-3 weeks. Current detection audit. Cost and accuracy analysis. Cascade design recommendations. Business case modeling.
FWA Pilot
$75K
6-8 weeks. Implement cascade for one claim type. Demonstrate detection rate and cost savings. Prove the model before full investment.
FWA Production Platform
$300K+
4-6 months. Complete cascade implementation. Integration with claims systems. Observability and governance. Team training and enablement.
Ready to do agentic AI right?
Your POC proved the concept. Let's build the production architecture that makes it sustainable.