Cognica Blog

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.

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.

Cognica ERP eliminates the complexity of data integration, instantly providing answers not only to 'what' but also 'why' and 'what's next,' creating business competitiveness.

On June 21, 2024, OpenAI announced the acquisition of database startup Rockset. According to OpenAI, the background of the Rockset acquisition is to improve search infrastructure to make AI more useful. Specifically, what advantages led OpenAI to acquire Rockset?

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

Many services are introducing recommendation systems to increase user retention time in modern applications, and this is an important factor directly related to sales, especially in content and e-commerce sectors. Recommendation systems analyze user behavior to understand their interests and provide related items, thereby increasing retention time and inducing purchases. How can vector databases be utilized in this context?

The mobile applications and web services we use have search functions. Most are developed using basic text search provided by databases or full-text search provided by search engines like Elasticsearch. Full-Text Search is one of the traditional methods mainly used for searching text data, focusing on finding specific keywords, words, phrases, etc., in documents, web pages, databases, and more. It typically involves inputting keywords or short sentences to search text data and finding documents that match the keywords, but it does not consider context or semantic similarity.