Case Study
Prism Insurance
Claims triage agent that routes and escalates with regulatory confidence.
InsurTech
60% faster claims
Outcome
$8.2M savings
Quantified Impact
Challenge
Claims agents spent hours documenting and categorizing incoming claims.
Strategy
Deployed an NLP-backed agent with policy-layer guardrails and explainability.
Execution
- • Built document extraction pipeline using vision models.
- • Trained intent classification on 50K+ historical claims.
- • Implemented human-in-the-loop validation for edge cases.
Tech stack
ClaudeLangChainTypesenseAWS LambdaNext.js
Results
- • 60% reduction in triage time
- • 94% accuracy on fraud flags
- • SOC2 audit trail for all decisions
Testimonial
"Regulators trusted the system because every decision was auditable and explained."
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