Everstream
Connects data sources, normalizes what comes out of them, and exposes the whole thing as tools an AI can call. Social accounts, websites, file uploads, Postgres, Firestore, Mongo, product analytics, Drive, ad accounts, voice recordings.
The point isn't the connectors. It's that an AI client learns one protocol instead of fifteen integrations, and that every tool it can see has already been narrowed to what the caller is allowed to touch.
- 01Fifteen source types, one protocol
- 02167 tools served as an MCP manifest
- 03Tokens narrow by workspace, collection, and stream
- 04Writes are two calls, never one
- 05Hybrid search: vector plus full-text
- 06Embeddings run in-process, not per-row API calls
In practice
A team connects a source once. It lands in a workspace as a typed stream, and from then on anything that needs it reads the stream instead of the database. An AI client fetches one manifest and gets the catalog, already filtered to what its credentials allow. Adding the next application is configuration, not another integration project.
- Databases
- Social accounts and websites
- Ad accounts and analytics
- Files, video, and audio
- Workspace, collection, stream
- Per-token tool allowlists
- Read-only unless opted in
- Any MCP client
- Internal tools and dashboards
- Copy-paste integration kits
Writes are two calls
A write tool doesn't write. It builds a preview and hands back a confirm id, and a second explicit call applies it. An AI can't mutate anything in a single hop, and anyone reading the transcript afterwards can see exactly what was about to happen before it happened.