Enterprise Retail & supply chain · IT operations
Work Order Intelligence Platform
AI Solution Architect & Lead AI Engineer · H-E-B
Agentic workflows that turn unstructured HVAC and refrigeration work orders into structured diagnostics, cost buckets and asset matches.
- cost-bucket accuracy
- 98%
- manual data entry
- −99%
The problem
Maintenance work orders arrive as free text. Diagnostics, asset details and costs were buried in prose and typed in by hand, so nobody could trust or analyze them at scale.
What I built and led
- Architected LangGraph agentic workflows that extract Problem–Cause–Remedy diagnostics from HVAC and refrigeration work orders, with agentic RAG over BigQuery vector search to match assets and components to the asset taxonomy and spec sheets.
- Built an LLM-as-judge evaluation pipeline that surfaced upstream data-quality issues and drove data-capture fixes at the source.
- Classified every work order into category-specific cost buckets: a JEV model (Typesafe AI) as the primary classifier, an LLM fallback below 0.9 probability, and a five-criteria evaluation step that ships, reviews or rejects each result.
- Designed a proof of concept for a multimodal (text and image) intake agent that replaces form-based requests: it gathers missing details, triages, and hands off each request with similar past incidents, likely duplicates and repair documents.
Results
- 98% cost-bucket accuracy
- −99% manual data entry