Factori MCP is a custom connector that plugs real-world intelligence directly into any AI agent like Claude, ChatGPT, or Cursor, directly to Factori's Real-World Graph.Once connected, your agent can query live data on people, places and movement across 229 countries, in plain english, no GIS software or SQL required.

How does Factori MCP help my team?

Factori MCP replaces days of manual location research with a single AI query. Instead of pulling foot traffic, demographics, and competitor data from multiple vendors and stitching it together manually, your team can ask a question in natural language and get a structured, real-world answer in seconds, powered by live data across people, places, and movement.

Do I need to be a developer or data engineer to use it?

No, the Setup is simple and super easy: Add the Custom Connector Generate your API key - https://console.factori.ai/home Once you've added your API key, you interact with Factori the same way you'd chat with an AI, just ask your question.

Which AI tools and platforms does it work with?

Factori MCP works with any MCP-compatible client including, Claude Desktop, ChatGPT, Cursor and LangChain. If your agent environment supports MCP, you can connect Factori to it.

What data is available today and what's coming?

Factori MCP currently spans five core categories: People & Communities, Places & Competition, Movement & Foot Traffic, Markets & Trends, and Site & Location Intelligence. Additional layers are continuously being added, including Events & Demand Signals, Property & Land Use, Retail Sales Indicators and Economic Indicators, enabling AI systems to answer an even broader range of real-world questions.

What kind of questions can I ask Factori MCP?

Here are a few examples of the kinds of questions you can ask: - "I'm opening a coffee shop near Atlantic Station, Atlanta. How many coffee shops are already there, what's the daily foot traffic, and what income bracket are the local residents?" - "What's the competitive density around Lenox Square Mall, and is the business quality matching the affluence of the neighborhood?" - "I want to open a gym near Ponce City Market. How saturated is the fitness market, and does the demographic profile support a premium offering?" - "What are the peak hours and busiest days of the week for the dining corridor near this address?" Factori MCP converts prompts like these into structured, real-world answers powered by live location intelligence.

Talk to the real world with Factori MCP - Get Started Now

Ground Truth

Name: Factori MCP
Brand: Factori
Availability: InStock

The Builder's Playbook for Physically-Grounded Agents

Ask an agent how many coffee shops sit near a corridor, or which of two neighborhoods gets more morning traffic, and it answers, fluently, specifically, and often wrong. This playbook is a 5-step method for closing that gap. Inside, you'll learn:

One graph of physical-world signal, built for AI agents to query

Mobility, places, and business data, corroborated against each other and reachable by any MCP-capable agent through an open standard. This is the data layer the method wires in at step two.

Observed, not modeled

Visit counts, POI counts, and brand detection are counted directly from real-world behavior. The playbook whitelists these for anything an agent states as fact.

Calibrated before it ships

Every field is checked against outside ground truth before it’s trusted, and the playbook is explicit about which fields pass that bar.

Queryable mid-answer

Reachable through the Factori MCP server, so a place question triggers a lookup instead of a guess, in the same turn.

Twelve ordinary questions, an ungrounded frontier model, zero measurements

Five Austin corridors and one LA retail destination. No tools, no retrieval, just the model’s best answer to the kind of question a retail-facing agent gets asked on day one.

0 / 12

verifiable measured values produced on its own

7 / 7

checkable answers that were materially wrong

the gap the model called "hard to say," wide enough to flip a site decision

A vendor-neutral method to close the gap

Why this isn't a prompting problem

The gap is a missing input, not a model defect. A longer prompt or a smarter model won't close it.

The 5-step grounding method

Baseline the failure, wire the data layer, constrain to observed fields, re-run and score, keep it closed.

The real numbers, worked example

Zero of twelve ordinary questions produced a verifiable measured value from an ungrounded model on its own.

Run it yourself: the eval harness

A 10-prompt test, run twice against your own agent, ungrounded then grounded, so you measure the gap yourself.

Free download

Get the full playbook

No fluff, straight to the 5-step method and the eval harness. Built for the builder shipping an agent that answers questions about physical places.