I have been building AI products for a while and kept running into the same problem.
Every project that involves querying documents with AI requires the same foundation before you can build anything interesting: a chunking strategy, an embedding pipeline, a vector database, re-ingestion logic when content changes, and a retrieval layer on top. It is not hard, it is just a lot, and it is not the part you actually want to be building.
So I built Kognita to handle it as a managed API.
You push content in via API, text or files, and get back hybrid search over a knowledge base. Kognita handles chunking, embedding, indexing, and automatically re-embeds when you update content. It is opinionated: we pick the embedding model and chunking strategy. The trade-off is less flexibility for a much faster path to a working knowledge layer.
What we are looking for
We want 10 teams who are building something that needs a knowledge layer and are willing to test it honestly. The ask is: what broke, what was confusing, what you needed that was missing.
Not looking for compliments. Looking for people who will actually use it and tell us where it falls short.
What you get
No credit card. No time limit. Higher than our standard paid plan.
Who it is for
Engineering teams building AI features over documents who do not want to manage the underlying infrastructure themselves. If you need full control over your embedding models or retrieval strategies, this is probably not the right fit. If you want to skip the pipeline and get to building, it might be.
How to get started
Sign up at kognita.io. Drop a comment here if you sign up and I will make sure you are on the early adopter tier.