Large language models made the internet fully queryable. They unlocked the ability to reason across vast amounts of text, code, and knowledge in seconds. But that understanding has always been limited to what exists online.
The physical world operates differently. It is dynamic, constantly changing, and largely unstructured. It is defined by how people move, where demand emerges, and how places evolve over time. These signals do not live on the internet, and as a result, they have remained outside the scope of how AI systems understand context.
We built the first MCP server for real-world data, so any AI agent can now query the physical world the way it queries the internet.
By structuring real-world signals across 229 countries into a unified, queryable layer, AI systems can move beyond static knowledge and access live, grounded context. The result is a shift from answering based on what was known to reasoning based on what is happening, bringing real-world awareness into every query.
“We built Factori MCP because we saw the same pattern over and over teams spending weeks integrating data pipelines just so their AI could answer basic location questions. One MCP call should be enough.”
Understand who lives, works, or moves through any area, demographics, behaviors, and daily patterns.
“Who lives within 5 miles of our new Seattle location?”
“What kinds of workers commute into downtown Austin on weekdays?”
Map the competitive landscape anywhere in the world, what businesses exist, how they cluster, who’s winning.
“What coffee shops compete within a mile of our proposed Shoreditch location?”
“Break down the retail landscape in San Francisco’s Marina District.”
See how people actually move, when they arrive, how long they stay, how patterns shift over time.
“Who lives within 5 miles of our new Seattle location?”
“What kinds of workers commute into downtown Austin on weekdays?”
Track how neighborhoods and regions evolve, what’s growing, declining, and where momentum is building.
“Who lives within 5 miles of our new Seattle location?”
“What kinds of workers commute into downtown Austin on weekdays?”
Get data-backed answers to your toughest location questions, where to open, close, expand, or invest.
“Who lives within 5 miles of our new Seattle location?”
“What kinds of workers commute into downtown Austin on weekdays?”
No data engineering. No pipelines. No SQL.
Create a free account and get your key in under a minute. No credit card required.
One command registers Factori as an MCP tool in your agent environment. Works with Claude, ChatGPT, Cursor, and any MCP-compatible client.
Ask about any location, in plain English. People, places, movement, markets, real data, back in seconds.
Two commands. Then just ask.
Registers Factori as an MCP tool in your agent environment.
claude mcp add factori — npx -y @factori/mcp-server
Get your free key at factori.ai/dashboard — no credit card required.
claude mcp add factori \
-e FACTORI_API_KEY=your-api-key-here \
— npx -y @factori/mcp-server
Also works with Claude Desktop, ChatGPT, Cursor, LangChain, and any MCP-compatible client.
# Ask anything about any location:
“What’s the foot traffic trend around Times Square over the past 90 days?”
More data layers are on the way — each one unlocks a new category of questions your AI can answer about the physical world.
Concerts, sports, conferences, and calendars that move foot traffic and demand.
“What’s the property mix within 1 mile of this site — residential vs. commercial vs. mixed-use?”
Parcel types, housing density, and land use classifications that define trade areas.
“What’s the property mix within 1 mile of this site — residential vs. commercial vs. mixed-use?”
Category-level spend signals showing where consumer spending is rising or softening.
“Is dining spend growing or declining in the Austin metro compared to 6 months ago?”
“Which QSR brands are gaining search momentum in Southeast Asia this quarter?”
“Is dining spend growing or declining in the Austin metro compared to 6 months ago?”
Chain vs. independent ratios, category composition, and competitive makeup by area.
“What’s the independent vs. chain restaurant ratio in Brooklyn, and how has it shifted?”
Income levels, employment rates, and local economic health signals that shape demand.
“How does the unemployment rate in this trade area compare to the metro average?”
Category-level spend signals showing where consumer spending is rising or softening.
“Is dining spend growing or declining in the Austin metro compared to 6 months ago?”
Join the early access waitlist and be among the first to integrate Factori MCP into your AI stack.
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