About the Project
We envisioned a GenAI agent that could act as an expert co-pilot for every engineer and technician. An agent capable of instantly parsing any technical document, understanding complex operational context, and delivering precise, actionable guidance to dramatically reduce mean-time-to-repair (MTTR) and prevent costly downtime.
The breakthrough came from a key insight: foundational Automated Test Equipment (ATE) concepts are timeless. While specific models change, the underlying principles of pin electronics, power supplies, and diagnostic methodology remain the same. A publicly available, 30-year-old HP 82000 manual became our "Rosetta Stone" to train an agent on universal troubleshooting principles, completely bypassing the proprietary data bottleneck that blocks most enterprise GenAI implementations.
Key Features
- Thought-Action-Observation Loop: LangChain ReAct Agent decides when to search documentation and how to synthesize answers.
- High-Fidelity Document Parsing: LlamaParse converts complex, multi-column PDFs with embedded tables and diagrams into structured, LLM-optimized markdown.
- Unified Error Code Database: Systematically extracts and consolidates every class of error code (ERRST, MCD, Test Function, BASIC) into a searchable knowledge base.
- Multi-Modal Retrieval: LlamaCloud extracts both text and visual context (diagrams, schematics) from technical PDFs.
- Full Observability: LangSmith captures every step of the agent's reasoning chain for debugging and monitoring.
- ROI-Driven Roadmap: Blueprint for scaling from pilot to enterprise-wide deployment with positive ROI within 12 months.
Tech Stack
Skills
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