Repair estimates across large fleet networks are notoriously inconsistent, hard to audit, and prone to overbilling. Payers lack real-time benchmarks and often rely on static rate tables or outdated rules-based logic.
One of the core challenges was the absence of a feedback loop between pre-repair estimates and actual invoice outcomes, making it difficult to identify cost anomalies, track patterns, or adjust payouts based on real data. Structuring invoice data at scale added complexity, particularly when dealing with a wide range of vendors, formats, and regional differences. Manual validation introduced delays, increased friction, and reduced trust in the process. Any viable solution needed to deliver the speed required for scale while maintaining enough control to support high-value, judgment-driven decisions.
Fendr.AI is currently in development, with pilot deployments underway. We’re building the system to scale across enterprise fleets, repair networks, and claims platforms.
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