Canonical Repair Profiler
Best Agentic Architecture (US & Europe) · Ford Agathon
A seven-agent LLM system that consolidates decades of noisy, free-text automotive warranty claims into a library of consensus "canonical repairs" — clean, confidence-scored ground-truth labels for downstream machine learning. A chain of single-responsibility agents shares one context and self-corrects through an LLM critic loop, combining statistical consensus with regional cost grounding, while all numeric outputs stay deterministic and auditable. Built with Rider Harrison; won Best Agentic Architecture (US & Europe) at Ford's Agathon hackathon, competing against 250+ teams.