Insurance / Claims

Intake claims with governed document intelligence.

Extract structured data from FNOL documents, photos, and correspondence — then route cases through policy-gated triage with full audit trail before adjuster assignment.

Claims Intake CopilotPilot
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Claims Intake Copilot

NeuroCluster runtime

Queue

Document review

Confidence · Evidence · ApprovalHITL

−40% target

Intake time

Policy-gated

Human review

100%

Audit coverage

The challenge

Claims intake teams process heterogeneous documents under strict data classification rules. Shadow AI tools bypass retention policies and create unauditable extractions.

How it works

A governed copilot classifies incoming documents, extracts fields with confidence scores, and routes low-confidence items to human review. Every extraction and routing decision is logged with model, policy, and approver context.

Outcomes

  • Faster first notice of loss processing with human checkpoints
  • Row-level access to sensitive claimant data
  • Exportable evidence for internal audit and regulator review

Capabilities

Document classification

FNOL, medical, and policy docs routed by type and sensitivity.

Confidence thresholds

Low-confidence extractions escalate to adjuster review.

Fraud signal surfacing

Pattern flags presented with evidence — no autonomous denial.

Retention policies

Data handling aligned to classification and jurisdiction.

Workflow

  1. 1

    Receive

    Ingest documents via secure channel with tenant isolation.

  2. 2

    Extract

    AI extracts fields; low-confidence items queue for review.

  3. 3

    Triage

    Policy gates route cases by severity, fraud signals, and coverage.

  4. 4

    Assign

    Approved cases export to claims management with audit pack.

Platform stack

Governed agentsRow-level accessHITL approvalsAudit evidence

The future of AI requires sovereign infrastructure, trustworthy reasoning and enterprise governance.