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.
Aeca, 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
See Aeca’s Technology in Action Through Real Case Studies
We propose a new approach to LLM usage by momentarily reconstructing the context.
by Jaepil Jeong | 2024-12-15
Explains the limitations and characteristics of vector embeddings and covers the improvements made to store them.
by Aeca Team | 2024-07-17
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?
by Tim Yang | 2024-07-11
Explains how to build a natural language search service by applying vector search to a case law search demo using FTS.
by Aeca Team | 2024-07-04
Experience Aeca Yourself with a Demo
Explains the process of downloading case law data and building a case law search service in just one day using Aeca.
by Aeca Team | 2024-06-21
We explain the process of data collection and processing, search, and service development for product search using Aeca. Learn how to index when structured and unstructured data are mixed, and how to transform queries for search using LLM.
by Aeca 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.