What MAD-Ai Did With a Backlog of Warranty Claims — Step by Step
- Rick Heilshorn
- 1 day ago
- 1 min read
A Tier 1 automotive supplier had a large backlog of unstructured warranty claims. Here's exactly what MAD-Ai did with them — and what it means for your quality team.
Step 1: The Problem
Hundreds of incoming warranty claims. Each one unstructured — different formats, different levels of detail, different terminology. A team of engineers reviewing them manually, working through a backlog that outpaced their capacity to close it.
Step 2: What MAD-Ai Did
Processed every claim. Applied the supplier's internal categorization logic — learned from past decisions, not explicit rules — and assigned each claim to the correct category. No prompt engineering. No rules input by the team.
Step 3: The Result
97.5% accuracy. Less than 2% flagged for human review due to low confidence. Everything else: done.
Step 4: What That Means Operationally
Your team reviews the exceptions instead of the entire backlog. That's not a time saving. That's a capacity transformation. Engineers focus on the 2% that genuinely requires their judgment — not the 98% that can be handled systematically.
"Based on an engineer not telling you how his brain thinks, and you can replicate that — that's amazing."
Step 5: What This Is
MAD-Ai doesn't summarize your warranty data. It does the categorization work — at scale, consistently, in a fraction of the time. This is what an AI workforce looks like.
Ready to see what MAD-Ai does with your data? → mad-ai.com/book-a-demo
Comments