63% reduction in first-pass manual triage effort
A national payer faced rising claim volume and inconsistent first-pass triage quality across adjuster teams. Radiantze built a Gen AI assistance layer that reads claim packets, surfaces policy-relevant passages, and recommends routing, without auto-adjudicating high-risk categories.
Radiantze scoped LangChain retrieval against approved policy corpora with version control and citation links on every model response. LangGraph orchestration defined explicit branches for straight-through suggestions, supervisor escalation, and hard stops when confidence or PHI handling rules failed validation.
Evaluation harnesses ran on production-representative claim samples each sprint. Precision on routing suggestions, citation accuracy, and latency against adjuster SLAs were tracked in dashboards operations leaders reviewed weekly.
Human review remained mandatory on consequential actions. Adjusters saw suggested codes and policy excerpts with one-click acceptance or override, and every override fed back into the evaluation set so the program improved without silent drift.
Security and privacy controls matched HIPAA expectations: tokenized logging, role-scoped tool access, and secrets managed outside application repos. Pre-launch review covered headers, TLS, and API abuse paths before external adjuster pilots began.
After hypercare, first-pass manual triage effort dropped 63% on in-scope claim types while compliance stakeholders retained full traceability from suggestion to final disposition. Radiantze handed off runbooks and monitoring ownership to the payer’s internal AI operations team.
