AppMatic Tech engineers AI claims-triage agents that classify insurance claims, gather policy and document context through governed tools, flag fraud signals, and prepare cases for adjusters, built with Node.js and LLM orchestration. Adjusters decide; the agent removes manual intake. 330+ products shipped since 2017.
Claims adjusters spend hours assembling documents before they can assess anything. AppMatic Tech builds an agent that triages the claim, pulls the policy and supporting context, and hands the adjuster a prepared case.
AI Agent Development Stack for InsurTech
| Layer | What AppMatic Tech uses |
|---|---|
| Agent runtime | Node.js orchestration, tool-calling LLM |
| Data access | Policy, claim, and document tools (often via MCP) |
| Analysis | Classification and fraud-signal surfacing |
| Workflow | Case preparation and adjuster handoff |
| Audit | Access and decision logging |
How AppMatic Tech Delivers This
- Connect the agent to policy, claim, and document systems.
- Classify and triage incoming claims.
- Surface fraud signals and gather supporting context.
- Prepare an adjuster case and hand off for the decision.
- Validate against reviewed claims and deliver with a support SLA.
Frequently Asked Questions
- Does the claims agent decide claims?
- No. AppMatic Tech builds the agent to triage and prepare claims for human adjusters, gathering context and surfacing fraud signals, so the decision stays with an adjuster while manual intake is removed.
- How does it access policy and claim data?
- AppMatic Tech gives the agent governed tools over policy, claim, and document systems, frequently through an MCP server, with every access logged for audit.
- Can it flag potential fraud?
- AppMatic Tech has the agent surface fraud signals from claim and document patterns for the adjuster to review, rather than making an automated fraud determination.
Further reading: AI Agent Development: Architecture Patterns from 12 Deployments