Inconsistent troubleshooting and fragmented knowledge
For collaborative robots and semiconductor equipment, differences in engineer experience led to variations in troubleshooting speed and quality. The knowledge required to diagnose and resolve issues was scattered across multiple sources, requiring engineers to manually search for relevant information each time a problem occurred. New or previously unseen errors were even more difficult to address due to the lack of established troubleshooting guidance.
Semantic search with context-aware response generation
We implemented an AI agent that analyzes the intent and context of user queries and converts them into vector embeddings for semantic retrieval. The agent searches historical error-code resolutions and troubleshooting guides based on similarity, then uses the retrieved information to generate context-specific responses through multi-turn conversations.
This enables engineers to move beyond simple document search and access relevant troubleshooting knowledge based on the symptoms or error codes they encounter.
Guiding engineers from error identification to resolution
Applied to recurring, structured error-code scenarios, the agent provides step-by-step guidance from the initial error to potential causes and recommended inspection and resolution procedures.
By providing consistent troubleshooting guidance regardless of individual experience, the agent can reduce human error and improve the consistency and quality of technical support. The approach also provides a foundation for extending AI-assisted troubleshooting to broader document workflows and customer support processes.
