Operating System for Physical World Repair
An AI-native operating system for diagnosing, guiding, and automating real-world repair workflows.
Overview
Problem: real-world repair relies on tribal knowledge, scattered diagnostics, and slow escalation loops.
Why it matters: faster recovery means less downtime, safer products, and scalable expertise.
For: hardware engineers, field technicians, repair ops.
Vision & Roadmap
- Phase 0: capture diagnostics and failure-mode ontology.
- Phase 1: agentic repair flows with human-in-loop gating.
- Phase 2: closed-loop automation and predictive guidance.
- Done means: repair time reduced by >60% at scale.
Architecture
Diagram placeholder: multimodal intake -> orchestration -> tooling.
- Multimodal ingestion (images, schematics, BOM).
- Knowledge graph of failure modes and fixes.
- Tool-aware agents for sequencing repair steps.
Stack: Python, graph RAG, vector DB, secure microservices.
Data & AI
- Sources: field logs, repair manuals, CAD metadata.
- Models: multimodal LLMs + fine-tuned embeddings.
- Evaluation: defect resolution accuracy, time-to-repair.
Progress
- Failure-mode taxonomy v1
- Prototype RAG workflow
- Agentic repair sequencing
- Technician feedback loop
Blockers: cross-team data availability.
Next 30/60/90: data agreements, pilot site, metrics review.
Learnings
- Technicians trust systems that cite evidence.
- Multimodal inputs cut resolution steps by half.
- Feedback loops need low-friction capture.