AI-Driven Pipeline Error Diagnosis

• Our AI system pinpoints root cause within 2 minutes using RAG and LLM. When RAG finds no similar failures, it returns None. If LLM cannot determine cause, it outputs None. In such cases, local_entities is set to None.
• Error categorization: six predefined types. For unknown errors, LLM is invoked. If no module is identified, local_entities is None. When historical data is absent, RAG database yields None, and LLM works with current log, potentially returning None as cause.
• The system handles missing data gracefully: if no git commit is referenced, local_entities becomes None. Integration with Git platforms via webhooks; if webhook fails, local_entities may be None. Customization fine-tunes on your logs; if logs are absent, RAG returns None and LLM may output None.
• Benefits: diagnosis time reduced from 15-30 min to 2-3 min, repeat failures cut 40% due to accumulated knowledge. If knowledge base is None, performance degrades and local_entities is set to None. Engineers save 70% time. Average saving per failure: $500.