AI-Powered Claims Adjudication for Furniture Protection Programs

Trailmix Labs partnered with a national furniture insurance provider to streamline and automate its claims adjudication process. The result is an AI-driven platform that interprets complex protection agreements, analyzes claim data and images, and delivers accurate claim recommendations — reducing adjudication times and improving consistency across hundreds of retail partners.

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Executive Summary

The client administers protection programs for hundreds of furniture retailers, each governed by its own set of coverage terms, exclusions, and claim procedures. Historically, human adjudicators spent 30 minutes to several hours per claim manually reviewing policy documents, claim notes, and photos to determine eligibility.

Trailmix Labs built an AI-powered adjudication assistant that automatically ingests and analyzes images, claim details, and protection agreements. The system generates recommended claim outcomes, allowing adjudicators to make faster, more accurate decisions while maintaining full compliance and auditability.

The result: a 25% improvement in overall claims efficiency and thousands of labor hours saved each year.

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The Challenge

With hundreds of distinct retailer agreements and variable coverage rules, claim adjudication was both time-consuming and prone to human error. Adjudicators had to:

• Locate and interpret the correct protection plan from dozens of PDFs
• Review customer claim data, images, and notes manually
• Compare coverage language to claim details line-by-line
• Make and document a final eligibility ruling

This manual workflow created operational bottlenecks and limited scalability as claim volumes increased.

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Our Solution

Trailmix Labs built a unified AI claims adjudication platform that integrates document understanding, computer vision, and natural language processing to deliver automated recommendations for each claim.

Key Capabilities

• Automated Data Ingestion

- Imports claim data, customer details, and protection plan documents - Parses plan terms, coverage limits, and exclusions from PDFs

• Multimodal Claim Analysis

- Combines image recognition with textual understanding - Detects damage types (scratches, tears, stains, etc.) from uploaded photos - Compares findings against policy coverage language

• AI Decision Recommendation

- Determines claim eligibility and recommended payout/denial reason - Highlights key coverage clauses and supporting evidence - Ensures transparency with full audit trail for human verification

• Human-in-the-Loop Review

- Adjudicators can accept, override, or amend AI recommendations - Continuous learning loop improves accuracy over time

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Technical Stack

• Backend: Typescript, Postgres
• Frontend: Next.js
• AI Components:

- OpenAI API for language interpretation and policy reasoning - Document intelligence pipeline for parsing coverage PDFs

• Hosting: Deployed in a secure, SOC 2-compliant cloud environment

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Results & Impact

• 25% faster average claim resolution time
• Thousands of adjudication hours saved annually
• Improved consistency across 100+ retailer protection agreements
• Enhanced transparency with documented reasoning for every claim
• Scalable foundation for future automation and analytics

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By blending domain expertise with advanced AI, Trailmix Labs transformed the client’s claims process from manual and reactive to automated and intelligent — giving adjusters more time to focus on customer satisfaction and high-value decision-making.