Why agents need a datalake

Ask a capable model a question about your own life and it will reason beautifully — about nothing. It has no idea what you decided last week, what is in your inbox, or how your project is actually structured. Every conversation starts cold.

The fix is not a longer prompt. It is durable, queryable context: a store of your documents, messages, code, and notes that an agent can search and recall on demand. That is what a datalake is — and historically it required a data-engineering team to stand one up.

Catalyst shrinks that to the scale of a person or a small team. Ingestion, indexing, and retrieval are handled for you, behind a clean interface your agents query directly. The result is an agent that remembers — and a memory that stays private to you.

We think this is foundational. The most useful agents will not be the ones with the cleverest prompts; they will be the ones with the best context. Catalyst is how we give them that.