About Cognica

Relational queries, full-text search, vector similarity, and graph traversal have been treated as separate problems requiring separate systems. We developed a new theory and proved otherwise — every operation reduces to set theory over posting lists, a single algebraic framework that unifies every paradigm.

We built Cognica because this proof deserves a production-grade implementation. A fully transactional database engine where SQL joins, BM25 scoring, HNSW search, and graph pattern matching share the same execution model, the same optimizer, and the same storage layer.

Our work is grounded in published research: a unified query algebra with formal completeness proofs, Bayesian score calibration that turns BM25 and vector scores into actual probabilities, and a Bayesian derivation showing that multi-signal fusion is a neural network. We do not bolt features onto existing systems. We derive them from first principles.