GenAI-powered Smart Maintenance Assistant
RAG chatbot for real-time troubleshooting
- User problem
- Maintenance teams were spending too much time hunting through logs, manuals, and sensor data to diagnose issues. Context was scattered across systems, and answers were slow—especially during incidents when speed mattered most.
- UX / UI approach
- A familiar chat interface so users could ask in plain language. Responses surfaced concise summaries plus source references (log snippets, manual sections, relevant sensor readings) so technicians could trust and verify answers. Streamed replies to reduce perceived latency and kept conversation history for follow-up questions.
- Frontend architecture
- React SPA with a message list and an input tied to a streaming API. State for conversation turns and loading/error UX; optimistic updates for sent messages. Responsive layout so it worked on tablets in the field. Accessible form and live region for streamed content.
- Backend / system design
- RAG pipeline: ingestion of maintenance logs, manuals, and sensor data into a vector store; retrieval of top-k chunks by query embedding; LLM (e.g. GPT/Claude) with retrieved context to generate answers. APIs for chat completion and streaming, with auth and rate limits. Chunking and embedding strategy tuned for technical content.
- Impact and metrics
- Faster time-to-answer for common troubleshooting queries; fewer context switches between tools; positive feedback from early users on relevance of retrieved sources. Qualitative wins on onboarding—new technicians could self-serve from documentation and past resolutions.
- Learnings
- Chunk size and overlap heavily affect retrieval quality for technical docs. Balancing latency (embedding + LLM calls) with completeness led to clear streaming UX. Tracing and evaluating LLM outputs against known good answers helped iterate on prompts and retrieval.