Hundreds of Warranty Claims. 97.5% Accuracy. Less Than 2% Flagged for Review.

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We ran a large batch of unstructured warranty claims through MAD-Ai for a Tier 1 automotive supplier. It categorized every one of them. Flagged less than 2% for human review due to low confidence. The rest were done.

The Setup

The supplier was processing hundreds of incoming warranty claims tied to a major OEM program. Each claim came in with different formats, different levels of detail, different terminology. A team of quality engineers had been reviewing them manually — reading, categorizing, routing. The backlog was growing.

MAD-Ai was given the same task. No explicit instructions on how to categorize. No rules written by the engineering team. The platform studied the pattern of past decisions — how the team had categorized similar claims before — and applied that logic to the new batch.

The Result

97.5% accuracy on real warranty data from a major OEM program. Less than 2% flagged for human review. Everything else: categorized, routed, done.

"Based on an engineer not telling you how his brain thinks, and you can replicate that — that's amazing."

What This Means Operationally

Your most experienced engineers make thousands of micro-decisions every week. MAD-Ai can learn that logic, apply it at scale, and free your team to handle the exceptions that genuinely need human judgment.

Instead of every engineer reviewing every claim, your team reviews the 2% that actually requires their expertise. That's not a time saving — it's a capacity transformation.

This is what an AI workforce looks like. Not a chatbot. A system that executes the work — at scale, consistently, with 97.5% accuracy on your actual data.

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See what MAD-Ai can do with your data → www.mad-ai.com/book-a-demo

#MADAi #WarrantyClaims #DataAnalytics #Tier1Supplier #SupplyChainQuality #ArtificialIntelligence #OperationalExcellence