The city as a source for the age of agents.
More and more people ask an AI where to eat, where to stay, what to do. iamin.city exists so every city has a source of organized, checked data that people and AI agents can consult without getting a guess. We're still building. The technical part that holds the idea up already works.
Today, to know about a place, you depend on whoever pays to show up or on an AI that guesses.
Guides and online directories (travel, restaurants, city) show first whoever pays to appear, with data that goes stale and nobody checks.
They answer with confidence even when they don't know: wrong hours, invented phone numbers, places that already closed.
Doesn't control what's said about it, or which channel the customer asks in.
A network of cities, each one a knowledge server.
The same city for people and for machines
A person asks on WhatsApp and gets the answer in the chat. An AI agent queries the city through the MCP protocol, the standard agents use to talk to data sources, and gets the information organized. One source, for two audiences. It's the base for a future where agents plan, recommend and book for us.
One city, then another, then the world
The same schema in every city: whoever builds an agent connects one city and already knows how to query them all.
What's ready to grow.
"Absent" instead of a guess
No answer can bring back what isn't in the base. If the data doesn't exist, it comes back as absent. That's not a request for the AI to behave, it's how the system was built.
A taxonomy that knows what a place feels like, not just what it is
Three layers for each place: what it is, what it has and what it is like to be there. The third, which no directory has, comes from real reviews, with a filter: only what comes up again and again, what the business is responsible for and what helps the reader makes it in.
A new city in an afternoon
A written, measured method: hundreds of businesses, map and pages in about two hours, with less than US$ 10 in data cost. This is one of them: 429 places on the map, each with what it is, what it has and what it's like.
A pilot in the making
A guide with a map, events and an assistant that answers on WhatsApp from the listings. It's the network's first lab.
From pilot to network.
Turning public listings into confirmed listings, one WhatsApp conversation at a time.
Opening the query to third-party AI agents, with the same schema in every city.
Testing who pays first: the local business, bookings or whoever consumes the data.