Aivonit — AI Motor & Auto Claims Platform

Aivonit automates motor and auto insurance claims end to end — from FNOL intake to settlement — with AI coverage analysis, computer-vision damage assessment, fraud-risk scoring, and an adjuster cockpit. Native integrations with Guidewire, Duck Creek, Sapiens, and Origami. Healthcare claims also supported on the same platform.

Lines We Serve

• Motor & Auto Insurance (primary) — FNOL notices, motor claims, adjuster reports, repair estimates, damage photos, and policy documents. AI coverage analysis with verbatim policy-clause citation, computer-vision damage assessment with repair-vs-total indication, fraud-risk scoring, and an end-to-end FNOL-to-settlement workflow.
• Healthcare (secondary) — Patient Records, Lab Reports, Insurance Claims, Prescriptions, Discharge Summaries, Medical Bills. ERA/835 processing and TPA connectors.

What Aivonit Does

• FNOL intake from email, drag-and-drop, cloud connectors, and APIs — with AI-recommended triage.
• AI coverage analysis that cites the exact policy clause applied to every decision.
• Computer-vision damage assessment, including total-loss indication, on accident and property photos.
• Fraud-risk scoring with explicit risk flags surfaced to the adjuster.
• Adjuster cockpit: AI recommendation, confidence, evidence chain, approve/override in one screen.
• FNOL-to-settlement workflow with SLA tracking and per-adjuster authority limits.
• Native policy/claims connectors: Guidewire, Duck Creek, Sapiens, Origami. Bulk CSV/Excel import or JIT REST webhook for everyone else.
• Full audit trail: every LLM call recorded with prompt, response, tokens, latency, cost — required for state DOI and unfair-claims-practices defensibility.
• Per-organization choice of LLM provider — OpenAI, Azure, AWS Bedrock, or Google Vertex AI. VPC-isolated deployment available for large carriers.

Get Started

Start a free trial or request a demo with your own claims documents. Learn more about integrations, browse the documentation, or read the frequently asked questions.