Build apps that answer from your own content without standing up a vector database or an embedding pipeline. Put documents in walled-off collections, opt in with one parameter, and get grounded answers, with embeddings and storage that never leave the EU.
collections · one parameter · EU-resident
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Behind a working demo sits a vector database to operate, an embedding pipeline to maintain, a chunking strategy to tune, and often a US embeddings API that ships your documents abroad. Then you still have to keep one team's content out of another's answers and prove where any of it was processed.
Paste or upload PDF, text, or markdown into a collection. The platform parses, chunks, embeds, and stores it in the background. No vector database to operate.
Each collection is its own index, a request reads exactly one, and embeddings and storage stay in the EU. Never another collection's documents, never another tenant's.
Opt in with one parameter and the most relevant passages are added as context. If retrieval cannot run, the request errors rather than answering without your documents.
A real retrieval app is more than a vector search. The modules it needs sit behind the same endpoint.
Create a key, add your documents to a collection, and ground your app with one API parameter.
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