Tytan: Interactive Neurosymbolic Construction of Analytic Semantic Schemas from Relational Data
Teaching AI to automatically map what's really in your databases
A new system called TYTAN can automatically figure out the structure and meaning of data in business databases by combining pattern-matching with AI language models, asking targeted clarifying questions when needed. In tests across eight real databases, it achieved perfect accuracy in finding every entity and executing data retrieval instructions correctly, and correctly identified semantic roles 92–100% of the time.
Most data analysis tools today require expensive hand-coded descriptions of what data means and how it connects — work that only specialists can do, takes weeks, and introduces errors. TYTAN eliminates this bottleneck, meaning businesses can stand up analytic systems faster, non-technical employees can query databases without waiting for expert help, and the process becomes more reliable.