Data processing infra & ETL for generative AI applications
For startups and enterprise companies building Generative AI solutions, Context Data automates the development of data processing, transformation (ETL) and scheduling infrastructure from an average of 2 weeks to less than 10 minutes and at 1/10th of the cost.
Hi Product Hunters, I'm Jide and I'm the founder and CEO of Context Data.
I started working on Context Data because I was spending too much time trying to write code to get data from my databases and S3 buckets, embed the data and then write it to my vector database especially if I needed the data to be refreshed and up-to-date.
In some cases, I was spending more time trying to stitch together multiple data sources together than I was spending building the actual RAG application.
So I decided to build simple infrastructure that allows anyone to quickly deploy data processing, ETL and scheduled data flows for their search and RAG projects in as little as 10 minutes.
Essentially, I set out to build the “FiveTran for Generative AI” where you can connect to multiple data sources, embed the data using all major embedding models and write the results to your vector database targets without having to write and rewrite tons of code.
With Context Data, you can:
- Connect to multiple external sources (MySQL, Postgres, S3, Salesforce etc.)
- Connect to multiple vector databases (Pinecone, Weaviate, Qdrant etc.)
- Perform cross platform transformations & ETL (e.g. joins and aggregations)
- Schedule recurring ETL jobs to vector databases for up-to-date data
- Connect to all of your vector databases in one click and run search and RAG queries on your data
All of this can be done without having to build any infrastructure, write code or hire expensive engineers.
Happy to answer any questions you may have!
Don't forget to upvote!
