Building an AI-first platform for root cause analysis combining large-scale user session intelligence, log analysis, and LLM-powered automation. Features similarity search with embeddings, automatic triaging, and innovative autofix capabilities using code insights and LLMs.
The Challenge
Modern applications generate massive volumes of logs, traces, and user session data. When something breaks, engineers spend hours manually correlating signals across systems to find root causes. The client needed an AI-first platform that could automatically triage issues and suggest fixes.
Our Approach
We built a pipeline combining Kafka for real-time ingestion, Milvus for similarity search across error embeddings, and PostgreSQL for structured metadata. LLM-powered agents analyze stack traces, correlate user sessions with backend errors, and generate actionable fix suggestions by understanding the codebase context.
Results
The platform automatically triages incoming issues, groups similar errors using embedding similarity, creates tickets with full context, and generates autofix suggestions — reducing mean time to resolution and freeing engineers from manual log-diving.