Automating semantic schema construction lets non-technical users query databases without expert help, and TYTAN's hybrid symbolic-LLM approach achieves production-ready accuracy by knowing when to ask humans for clarification.
TYTAN automatically builds semantic schemas for relational databases by combining symbolic analysis with LLM inference to identify entities, relationships, and data roles. It asks targeted questions when ambiguous, achieving 100% coverage and correctness on real-world databases—eliminating the manual work that currently bottlenecks data analysis tools.