📐 Design Studio

The User Understanding system processes user data through a multi-stage pipeline that transforms raw information into structured knowledge. When data streams are ingested, they undergo a comprehensive backfill process that extracts multidimensional insights across various temporal granularities using specialized LLM prompts. These insights are stored as structured observations in indexed databases, creating a rich knowledge foundation.

 At query time, the system leverages a sophisticated RAG implementation combining BM25 lexical search, embedding-based semantic similarity, and temporal context expansion. What makes this architecture particularly powerful is its self-questioning mechanism that proactively generates and answers questions from multiple perspectives, creating a dense network of pre-computed insights. When user queries arrive, this approach enables retrieval of both raw evidence and previously synthesized observations, allowing for more comprehensive and contextually rich responses.