Database that creates
new experiences and optimal
new experiences and optimal
Cognica is a multi-purpose OLTP database, a new type of database that offers a variety of features with high performance. Cognica makes it easy to develop reliable, scalable applications with full-featured Full-text Search and Vector Search, as well as the functionality of common databases.
Give it a try with Cognica
You can implement an AI search with your own data.
If you wish to create a generative AI search based on your data (internal documents, customer inquiries, product information, etc.), you can store the data into Cognica and combine it with the LLMs such as ChatGPT. Learn more about how to implement a search engine with Wikipedia data.
Implement search without using a dedicated search engine.
Don't you find it difficult and expensive to integrate a search engine? Don't worry, you can use the Full-Text Search feature directly from the Cognica database. Regardless of the size of company, you can easily build the search function you want. Also, this can help you save server costs and and development resources all at once.
Build your own recommendation system, including shopping items, movies, books, travel destinations, fashion, and even job candidates.
Regardless of the data type, the architecture of implementing a recommendation system is similar. For example, by embedding document data such as books and magazines in paragraphs (representing data in high-dimensional vectors), you can search for books that contain similar content and recommend books that ideally suit your preferences.
You can also search for video, audio, and image data with the Cognica.
You can store all kinds of data as vector embeddings into Cognica and find it directly as vector searches. You can also search for images by word or find specific images in the video. On the other hand, you can find documents that express any particular images. If you want to implement an application with data such as these, Cognica is an ideal solution for you.
Additional Use Cases
Fraudulent transaction and fraud detection
- The ability to search for similarities means that outliers are also searchable.
- By embedding information including e-commerce online payments and financial transactions in real time, abnormal transactions are detected when an embedding contradicting from that of a normal transaction is searched.
- Since expressions and words used in legal documents such as precedents are different from that of conventional expressions, there is a limit to existing text search alone. Even if you make a search using the same or similar words, the content of the searched precedents may yield contrasting results from the events seared for.
- Combining text-based full-text search with vector search, Cognica's hybrid search can quickly identify hundreds of thousands of precedents, similar events in laws and regulations, and related precedents.
Job Candidate Recommendation and Job Search
- Even if you don't pre-categorize information such as skills, occupations, and certificates related to the job posting, we recommend the best candidates for the positions posted.
- For example, financial professionals and accountants need to be processed as synonyms because while keywords are contrasting, vector search can easily determine that the two occupations are of similar.
Travel and accommodation
- Instead of looking for a place to stay by setting up a filter for the various conditions offered by the service provider, the user can make a search for the conditions you want in natural language or can upload the images of the accommodation to yield the ideal product searches you.
- Even with the theme or shape of the accommodation image, you can find image similarity using vector search and obtain a list of products ideally suited.