Blog

Engineering notes on databases, search, and AI infrastructure by the Cognica team.

All Insights Tech Research Engineering Case Studies Company

The Missing Layer: A Structural Gap in the AI Infrastructure Stack

by Jaepil Jeong | April 10, 2026

Between models and hardware lies an invisible structural gap — a data layer that should exist but doesn't. We examine why the current AI infrastructure stack is fragmented, why this is a structural problem rather than a transitional one, and what conditions a proper solution must satisfy. We then present UQA and the Cognica engine as one concrete response to these conditions.

Read Post

Tech5 min read

Graph Queries in a Unified Database: From Cypher to Posting Lists

by Jaepil Jeong | March 26, 2026

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.

Read Post

Research7 min read

Vector Scores Are Not Probabilities: Likelihood Ratio Calibration for Hybrid Search

by Jaepil Jeong | March 25, 2026

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.

Read Post

Research11 min read

Why Sigmoid? The Mathematical Inevitability Behind Bayesian BM25

by Jaepil Jeong | February 23, 2026

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.

Read Post

Tech15 min read

Building a Probabilistic Search Engine: Bayesian BM25 and Hybrid Search

by Jaepil Jeong | February 1, 2026

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.

Read Post

Tech18 min read

JIT Toolchain: Building a Disassembler and CPU Emulator for Database Development

by Jaepil Jeong | January 19, 2026

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.

Read Post

Tech16 min read

Copy-and-Patch JIT: Achieving Native Code Performance with Microsecond Compilation

by Jaepil Jeong | January 17, 2026

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.

Read Post

Insights4 min read

An AI Database That Works Identically On-Device

by Tim Yang | December 23, 2025

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.

Read Post

Insights5 min read

Structural Limitations of Legal Case Search and the Need for Single DB with Vector Search

by Tim Yang | December 9, 2025

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.

Read Post

Engineering12 min read

Automated Financial Statement Extraction from PDFs Using LLMs

by Cognica Team | November 18, 2025

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.

Read Post

1 / 3