Unnamed global consumer brand automates 90% of warranty claims and delivers 220% ROI in 12 months
A modular AI agent combining UiPath RPA and OpenAI LLMs auto-validates warranty claims in near real-time, saving $250,000+ annually and resolving claims up to 70% faster—while routing only high-value cases to human reviewers.
Background
Support staff at a high-volume consumer goods company were manually verifying purchase details, applying complex warranty policy rules, and resolving escalations for every incoming claim. The process took two to three days per case, produced inconsistent outcomes, and could not scale with growing claim volumes.
What Was Implemented
- Modular AI agent combining UiPath RPA and OpenAI LLM for end-to-end warranty validation
- Automated eligibility checks: product details, purchase date, warranty duration, policy rules
- Human-in-the-loop escalation: claims over $100 threshold routed to human reviewers; all others processed autonomously
- GenAI document extraction pulling claim data directly from PDFs
- Automated email notifications for approvals, rejections, errors, and gaps
- Modular product data services allowing policy/price updates without modifying the agent
- Deployed on UiPath cloud with RBAC and 99.9% uptime SLA
Results
The solution automated 90% of all warranty claims and reduced per-claim resolution time by approximately 70% , collapsing turnaround from two to three days to near real-time. Annual cost savings are estimated at $250,000+ . The low-code, modular architecture delivered a 220% ROI within 12 months through reduced manual review, fewer error-related claim costs, and minimal maintenance overhead. Agent utilization reached 95%+ , with virtually all inbound claims routed through the AI agent since go-live.
Lessons
- Modular architecture (data services separated from agent logic) allows policy updates without redeployment, significantly reducing ongoing maintenance costs
- A dollar-threshold escalation rule (claims over $100 to humans) creates a defensible, auditable human-in-the-loop boundary without sacrificing automation rate
- GenAI document extraction eliminates structured data entry as a bottleneck for claim intake
- Vendor-commissioned case studies with anonymous clients should be verified against independent sources before treating ROI figures as benchmarks