Jeevesy isn't a property platform with a chatbot glued on top. Every domain object, workflow and policy is designed as a tool an agent can reason over — with MCP as the protocol, LLMs as the runtime, and RLS-scoped identity as the boundary. The dashboard is a fallback for the humans who still like clicking.
Cases, bookings, rent, documents, journeys and communications are exposed as typed, permissioned MCP tools. The web app calls them. Jeeves calls them. ChatGPT, Claude and Cursor call them. There is no second, "AI-only" API — because there is no non-AI path.
MCP is the open door for ChatGPT, Claude, Cursor and any future agent. On top of that, Jeevesy plugs directly into Apple Intelligence & the new Siri in iOS 27 via App Intents, and into Google Assistant & Gemini via App Actions and Connected Apps — so residents can raise a case, pay rent, book the padel court or ask about a leak without ever opening the app.
We ship the assistant surfaces incrementally so you always know what production tenants can rely on right now versus what's in flight.
Most "AI" property tools bolt a chatbot on top of a legacy CRUD app. Jeevesy inverts that: the data model, the workflows and the interfaces are all designed to be driven by autonomous agents — with humans in the loop where it matters.
Every entity — case, booking, contract, invoice, journey — is an MCP tool with a typed schema and RLS policy.
Case triage, category detection, sentiment, translation and drafting run through the LLM by default, not as opt-in add-ons.
Move-in, move-out, renewals and reminders run as agent workflows with tool calls, retries and human-in-the-loop approvals.
The same concierge lives in the tenant app, the operator dashboard, WhatsApp, email and any external MCP client.
Connect ChatGPT, Claude, Cursor, or an internal agent framework. Connect other systems as MCP servers back into Jeeves.
Every dashboard page is generated from the same tool schemas the agents use. One source of truth for humans and machines.
Notices, replies and translations are retrieved and grounded in property knowledge — no hallucinated policies.
Adoption, deflection, sentiment and SLA metrics feed back into prompts and journey policies automatically.
Mutating tools require approval, every action is audited, and secrets never enter the model context.
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