Cognica Blog

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.

The Monic framework addresses REST's endpoint explosion problem by allowing clients to express their intent as computational expressions through a single /compute endpoint, redefining APIs as a "Computational Interface" integrated with the database.

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.

Methods to overcome the limitations of Large Language Models (LLMs) by utilizing Vector Databases (VectorDBs) are gaining attention. To provide accurate answers on specialized information such as law firm case precedents or company communication records—domain data that is not included in the training data—we can use a Vector Database that can convert, store, and search all kinds of data into vector embeddings, serving as a long-term memory storage for LLMs. To illustrate this, we examine a concrete case of how a vector database can complement an LLM through processes like data preprocessing, vectorization, storage, and search, using a Q&A system based on Wikipedia.

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?