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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.

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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.

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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.

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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.

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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.

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Insights3 min read

Distributed Databases: A Structural Constraint in the AI Era

by Tim Yang | November 17, 2025

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.

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Insights6 min read

Why a Single Database?

by Tim Yang | November 11, 2025

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.

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