See Cognica’s Technology in Action Through Real Case Studies
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.
by Jaepil Jeong | 2026-01-17
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.
by Tim Yang | 2025-12-23
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.
by Tim Yang | 2025-12-09
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.
by Cognica Team | 2025-11-18
A Tech Builder Specializing in AI for Data and Search, Focused on Problem Solving
Improving Search Performance
Search results are inaccurate, and long processing times are frustrating users.
Data Overload
Excessive data is slowing down service, causing customer attrition.
Server Cost Optimization
Server costs are skyrocketing, making budget management challenging.
Embedding Processing
Choosing an embedding model is difficult, and it takes too long and costs too much.
Scalability Issues (Traffic Surge)
Sudden traffic increases pose a risk of service disruption.
Development Delays
You need to launch your product quickly, but there's a shortage of developers or time.
Cognica, Combining Databases and AI Technology
Single Database
Solve data models, search, and caching all in one
Huge Data
Process up to 100TB of data with a single database, no distribution needed
No Sync
No need for storage synchronization, sharding, or clustering
No Latency
Process 300,000 queries per second in real time
(*) When retrieving data from the database
Low Cost
Reduce server costs, data product usage, and developer resources by 90% compared to traditional infrastructure
Quick Dev.
Build application infrastructure with a minimal development team, and operate without increasing the learning curve
Experience Cognica Yourself with a Demo
Explains the process of downloading case law data and building a case law search service in just one day using Cognica.
by Cognica Team | 2024-06-21
We explain the process of data collection and processing, search, and service development for product search using Cognica. Learn how to index when structured and unstructured data are mixed, and how to transform queries for search using LLM.
by Cognica Team | 2024-06-12
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.