Cognica Blog

Modern search systems struggle to combine lexical matching with semantic understanding. We explore how we built a probabilistic ranking framework in Cognica Database that transforms BM25 scores into calibrated probabilities, enabling principled fusion of text and vector search results.

We examine the database architecture changes required by on-device AI. Just as SQLite was the answer for on-device computing, on-device AI requires a new database that integrates transactions, analytics, full-text search, and vector search. We explain why Cognica works identically on-device and on servers.

This article provides a technical analysis of why legal case search is challenging in the legal services market. We examine the structural characteristics of legal case data and the limitations of existing distributed architectures (RDB + ElasticSearch + Vector DB), and explain why integrated search based on a single database is necessary.

Exploring how function-based distributed database architectures become structural constraints in the AI era. We examine the limitations and complexity of traditional approaches combining OLTP, OLAP, FTS, and Vector DB, and introduce Cognica's unified database as a technical turning point.

Moving beyond the complexity and limitations of the era of specialized databases, we explore why a unified database is now essential. Discover the value of Cognica's integrated database that provides OLTP, OLAP, cache, FTS, and vector search in a single engine, and the future direction of databases in the AI era.

We discuss why NOT operations are difficult in vector search.

Explains the limitations and characteristics of vector embeddings and covers the improvements made to store them.

Explains how to build a natural language search service by applying vector search to a case law search demo using FTS.

You can easily create RAG (Retrieval Augmented Generation) with just one AI database without complex infrastructure setup.