Cognica Blog

Graph databases solve relationship-heavy problems elegantly, but adding a separate graph system alongside your relational database creates operational complexity. We explain how Cognica integrates graph queries into its unified algebra, enabling Cypher and SQL to compose in a single transaction without data duplication.

A cosine similarity of 0.85 tells you an angle, not a probability. We show how to transform vector similarity scores into calibrated relevance probabilities using distributional statistics that ANN indexes already compute — completing the probabilistic unification of text and vector retrieval.

Sigmoid is not a design choice — it is a mathematical theorem. We show why the sigmoid function is the unique valid transform for converting BM25 scores to probabilities, completing Robertson's Probability Ranking Principle after 50 years.

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.

The essential infrastructure that makes Copy-and-Patch JIT development and debugging practical. We explore the multi-architecture disassembler for validation and software CPU emulator for cross-platform testing and debugging.

How Cognica Database Engine breaks the JIT compilation latency barrier. We explore Copy-and-Patch JIT compilation, a technique that achieves 2-10x speedup over interpretation while keeping compilation time under one millisecond per kilobyte of bytecode.

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.

We introduce the process of building a system that automatically extracts and normalizes financial statements from PDFs in various formats using Large Language Models (LLMs). We cover data model design with Structured Output and Pydantic, the extraction process through Google Gemini API, and post-processing methods applicable to real-world scenarios, all implemented in about 200 lines of code.

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.