Trusted by teams building with agents
// Products
The world's physical data, structured for agents.
Four live products. Query via API, MCP, or download.
GPS MOBILITY
Footfall Data
How people move - origin, destination, dwell time.
Aggregated, privacy-safe foot traffic from mobile signals. Hourly patterns by location, origin-destination pairs, catchment analysis. 100m spatial resolution.
100m
SPATIAL RESOLUTION
Hourly
TEMPORAL GRAIN
Privacy-safe
AGGREGATED SIGNAL
CAR TRAFFIC
Road Traffic Data
Vehicle flow, road density, congestion - at any hour.
Segment-level traffic counts, speeds, and congestion scores. Historical trends and live feeds. Built for logistics routing, retail access scoring, and urban planning.
Every road
SEGMENT COVERAGE
15min
GRANULARITY
Live
FEED AVAILABLE
For thirty years, maps were made for humans to look at. Now they need to be read by machines that act.
// YESTERDAY
Static maps. Humans planning.
// TODAY
Dashboards. Humans deciding with data.
// TOMORROW
Agents querying the physical world directly. xMap is the layer they query.
05 / Use Cases
What can you build on xMap?
Every industry that touches physical space is being rewritten by agents.
Hospitality
Hospitality
"Score 50 Nashville addresses for a fast-casual concept. Surface the top 5 with rationale."
Nashville, TN - 50 addresses ranked in 4 minutes.
poi
competition
events
See full case
Retail Site
Retail Site
Where in Austin should I open a third location? Optimize for foot traffic, minimize cannibalization.
Austin, TX - agent outperformed broker 3 of 3 markets.
mobility
competitio
poi
See full case
Real Estate
Real Estate
"Find me 5 underpriced parcels in Brooklyn within 10min of a subway, foot traffic > 10k/day."
Find me 5 underpriced parcels in Brooklyn within 10min of a subway, foot traffic > 10k/day.
parcel_price
mobility
access
demographics
See full case
Climate
Climate
"Identify urban heat island clusters in Phoenix correlated with low-income demographics."
Phoenix, AZ - 14 priority zones identified.
buildings
demographics
mobility
See full case
Tourism
Tourism
"Build a 3-day NYC family itinerary optimizing transit time."
Manhattan, NYC - itinerary generated in seconds.
poi
mobility
events
access
See full case
Energy & Utilities
Energy & Utilities
"Site EV charging stations across Denver capturing 80% of commute traffic."
Denver, CO - 18 sites shortlisted from 2,400 parcels
car_traffic
parcel_ownership
access
See full case
Out-of-Home Ads
Out-of-Home Ads
"Rank 200 LA billboards by 18–34 impressions during weekday drive-time."
Los Angeles, CA - media buy optimized across 200 faces.
mobility
demographics
Out-of-Home Ads
See full case
Telecom
Telecom
"Recommend 5G small-cell placements across SF that maximize coverage and minimize permit risk."
San Francisco, CA - 14 optimal sites found.
parcel_ownership
buildings
mobility
See full case
Urban Planning
Urban Planning
"Show me the 20 intersections in Chicago with the worst pedestrian-vehicle conflict over 90 days."
Chicago, IL - review time cut 70%
car_traffic
mobility
events
See full case
Banking
Banking
"Validate the collateral profile of this mortgage against neighborhood comps and traffic trends."
Chicago, IL - LTV analysis with 6 xMap signals
parcel_price
mobility
demographics
See full case
Insurance
Insurance
"Flag all commercial parcels in flood zones in the Houston metro under our book."
Houston, TX - 3,800 at-risk parcels identified.
parcel_ownership
buildings
access
See full case
Logistics
Logistics
"Every industry that touches physical space is being rewritten by agents."
Miami, FL - −22% avg delivery time in 90 days.
car_traffic
access
parcel
See full case
06 / Agent Story
A real estate investor's agent evaluates a plot on Bleecker St, NYC.
07 / Why xMap
AI-native by design.
Built for agents, not analysts. Every dataset is queryable through MCP, REST, and SQL - no GIS expertise required.
Agentic from day one.
Our datasets ship with agent-ready schemas, embeddings, and tool definitions. Plug into Claude, GPT, or your own stack in minutes.
The physical world, archived.
We ingest, normalize, and version-control the real world so your agent's view of it is never stale.
08 / Built for the Agent Stack
Works wherever your agent lives.
Native MCP server, Python SDK, and REST API. Plug into Claude, GPT-4o, or your own stack in minutes.
Claude
GPT-4o
Gemini
LangChain
CrewAI
from xmap import Layer, Agent
agent = Agent(api_key=os.environ["XMAP_ KEY"])
result = agent.evaluate(
parcel="314 Bleecker St, NYC",
objective="cafe_concept",
layers=["mobility","competition",
"demographics"," parcel_ price",
"attractions"],
radius_m=500,
)
print(result.decision) # "PROCEED"
print(result.confidence) # 0.84
print(result.recommended_action)# "lease, not buy"
agent = Agent(api_key=os.environ["XMAP_ KEY"])
result = agent.evaluate(
parcel="314 Bleecker St, NYC",
objective="cafe_concept",
layers=["mobility","competition",
"demographics"," parcel_ price",
"attractions"],
radius_m=500,
)
print(result.decision) # "PROCEED"
print(result.confidence) # 0.84
print(result.recommended_action)# "lease, not buy"
// claude_desktop_config.json
{
"mcpServers": {
"xmap": {
"command": "npx",
"args": ["-y", "@xmap/mcp-server"],
"env": { "XMAP_API_KEY": "xm_..." }
}
}
}
// Tools available automatically:
// xmap_poi_search · xmap_mobility_query
// xmap_parcel_lookup · xmap_traffic_flow
// xmap_demographics_profile
{
"mcpServers": {
"xmap": {
"command": "npx",
"args": ["-y", "@xmap/mcp-server"],
"env": { "XMAP_API_KEY": "xm_..." }
}
}
}
// Tools available automatically:
// xmap_poi_search · xmap_mobility_query
// xmap_parcel_lookup · xmap_traffic_flow
// xmap_demographics_profile
curl -X POST https://api.xmap.ai/v1/evaluate \
-H "Authorization: Bearer xm_..." \
-d '{
"parcel": "314 Bleecker St, NYC",
"objective": "cafe_concept",
"layers": ["mobility","competition",
"demographics","parcel_price"],
"radius_m": 500
}'
# → { "decision": "PROCEED",
# "confidence": 0.84,
# "reasoning_trace": [...] }
-H "Authorization: Bearer xm_..." \
-d '{
"parcel": "314 Bleecker St, NYC",
"objective": "cafe_concept",
"layers": ["mobility","competition",
"demographics","parcel_price"],
"radius_m": 500
}'
# → { "decision": "PROCEED",
# "confidence": 0.84,
# "reasoning_trace": [...] }
// Access the data
Two ways to use xMap data.
Query in real time, or download the dataset. Both live today at poi.xmap.ai
API & MCP
Query in real time from any agent, IDE, or codebase. Returns structured JSON with 30+ fields per location. p95 latency under 200ms.
# MCP - Claude Desktop / Cursor
https://api.poi-data.com/mcp/
YOUR_API_KEY
# REST
GET /v1/search?q=coffee
&near=40.730,-73.935&radius_m=500
https://api.poi-data.com/mcp/
YOUR_API_KEY
# REST
GET /v1/search?q=coffee
&near=40.730,-73.935&radius_m=500
Claude
GPT-4o
Cursor
LangChain
REST
24M US POIs live now · API + MCP + download · poi.xmap.ai
09 / Architecture
How xMap fits into your agent stack.
Every source flows to every layer. Every layer flows to every integration.
const response = await claude.messages.create({
model: "claude-opus-4-5",
tools: xmap.mcp_tools(),
messages: [{
role: "user",
content: "Evaluate 314 Bleecker St for a café"
}]
});
// Claude calls xmap_poi_search,
// xmap_mobility_query, xmap_parcel_lookup
// automatically - no routing needed.
model: "claude-opus-4-5",
tools: xmap.mcp_tools(),
messages: [{
role: "user",
content: "Evaluate 314 Bleecker St for a café"
}]
});
// Claude calls xmap_poi_search,
// xmap_mobility_query, xmap_parcel_lookup
// automatically - no routing needed.
from xmap import Agent
agent = Agent(api_key=os.environ["XMAP_KEY"])
result = agent.evaluate(
parcel="314 Bleecker St, NYC",
objective="cafe_concept",
layers=["mobility","competition",
"demographics","parcel_price"],
radius_m=500,
)
print(result.decision) # "PROCEED"
print(result.confidence) # 0.84
agent = Agent(api_key=os.environ["XMAP_KEY"])
result = agent.evaluate(
parcel="314 Bleecker St, NYC",
objective="cafe_concept",
layers=["mobility","competition",
"demographics","parcel_price"],
radius_m=500,
)
print(result.decision) # "PROCEED"
print(result.confidence) # 0.84
SELECT p.parcel_id, p.address,
m.daily_foot_traffic,
v.est_value_sqft,
COUNT(c.poi_id) AS competitors
FROM xmap.parcels p
LEFT JOIN xmap.mobility m
ON p.geom && m.bbox AND m.window='30d'
LEFT JOIN xmap.parcel_values v
ON p.parcel_id = v.parcel_id
LEFT JOIN xmap.poi c
ON ST_DWithin(p.geom,c.geom,500)
AND c.category='cafe'
WHERE p.city='New York'
AND m.daily_foot_traffic > 10000
GROUP BY 1,2,3,4;
m.daily_foot_traffic,
v.est_value_sqft,
COUNT(c.poi_id) AS competitors
FROM xmap.parcels p
LEFT JOIN xmap.mobility m
ON p.geom && m.bbox AND m.window='30d'
LEFT JOIN xmap.parcel_values v
ON p.parcel_id = v.parcel_id
LEFT JOIN xmap.poi c
ON ST_DWithin(p.geom,c.geom,500)
AND c.category='cafe'
WHERE p.city='New York'
AND m.daily_foot_traffic > 10000
GROUP BY 1,2,3,4;
09b / Integrations
700+ full integrations.
Your agents, connected.
xMap agents don't just read data - they act. Connect to the tools your team already uses and let agents write CRM records, trigger workflows, push to warehouses, and notify stakeholders the moment a site decision is made.
pre-built connectors
700+
Figma
Dropbox
Zoom
Databricks
Intercom
Twilio
MongoDB
PostgreSQL
Amazon S3
Slack
Microsoft Teams
HubSpot
Salesforce
Jira
QuickBooks
Airtable
Xero
Google Drive
Notion
Linear
GitHub
Anthropic
OpenAI
Stripe
Snowflake
Zendesk
Asana
ClickUp
Confluence
Discord
Figma
Dropbox
Zoom
Databricks
Intercom
Twilio
MongoDB
PostgreSQL
Amazon S3
Slack
Microsoft Teams
HubSpot
Salesforce
Jira
QuickBooks
Airtable
Xero
Google Drive
Notion
Linear
GitHub
Anthropic
OpenAI
Stripe
Snowflake
Zendesk
Asana
ClickUp
Confluence
Discord
Figma
Dropbox
Zoom
Databricks
Intercom
Twilio
MongoDB
PostgreSQL
Amazon S3
Slack
Microsoft Teams
HubSpot
Salesforce
Jira
QuickBooks
Airtable
Xero
Google Drive
Notion
Linear
GitHub
Anthropic
OpenAI
Stripe
Snowflake
Zendesk
Asana
ClickUp
Confluence
Discord
Figma
Dropbox
Zoom
Databricks
Intercom
Twilio
MongoDB
PostgreSQL
Amazon S3
Slack
Microsoft Teams
HubSpot
Salesforce
Jira
QuickBooks
Airtable
Xero
Google Drive
Notion
Linear
GitHub
Anthropic
OpenAI
Stripe
Snowflake
Zendesk
Asana
ClickUp
Confluence
Discord
Auth handled
OAuth, API keys, JWT - managed automatically per provider.
Rate limits
Smart throttling and retry logic baked in, no config needed.
Pagination
Cursor and offset-based pagination abstracted away.
Webhooks
Real-time event streaming with signature verification.
10 / Live Demo
Try it. No signup.
Type a question. Watch the agent query the world.
11 / Case Studies
In production with builders shaping the next decade.
National Real Estate Fund
NYC · Boston · Miami · Austin · LA · Chicago · Seattle · Denver
Cut deal-screening time from 14 days to 90 minutes.
A top-25 US real estate fund deployed an internal AI agent on xMap's parcel, mobility, and demographics layers to pre-screen acquisition targets across 9 metros. The agent evaluated 4,200+ parcels per week.
14d → 90min screening
4,200 parcels/wk
＋38% deal pipeline
"xMap is the only data layer we found that actually ships with agent-ready schemas. We had a working prototype in a day."
- CTO, US Real Estate Fund
Read the full case study →
National QSR Brand · 1,400+ US locations
8 head-to-head markets
Site selection model that out-performed brokers in 7 of 8 markets.
A QSR brand replaced their broker-led site selection process with an agent on xMap's mobility, competition, and demographics layers. Head-to-head test across 8 markets - agent won 7.
7 of 8 markets won
$2.1M avg saved/site
＋19% Y1 store revenue
"The agent surfaces things a broker's gut can't - traffic patterns at 7am on a Tuesday."
- VP Real Estate, QSR Brand
Read the full case study →
National Last-Mile Logistics Platform
Top 50 US metros
Reduced average delivery time 22% in 90 days.
A same-day delivery platform integrated xMap's car-traffic, buildings, and parcel layers into their hub-placement and routing models, redesigning their network across 50 metros in 90 days.
−22% avg delivery time
＋11pts NPS
$48M annual route savings
"xMap gave us ground truth on traffic patterns we couldn't get anywhere else at this resolution."
- Head of Network Engineering
Read the full case study →
// Success Stories
The world's most data-driven companies choose xMap.
Hover any card to see what they built.
Rakuten
Japan - Communication
To advance AI-driven geospatial intelligence for network deployment and market analysis
collaborated with Rakuten Symphony to advance AI-driven geospatial intelligence for network deployment and market analysis. The partnership focuses on transforming data into actionable intelligence for telecom infrastructure and market segmentation.
POI
Football
Demographics
Sovereign Wealth Fund
Unilever
FMCG · Japan
xMap emerged as the trusted provider of restaurant data in the Middle East and Africa, offering a tailored solution to Unilever's unique data-sourcing challenge.
By leveraging xMap's datasets, Unilever achieved a transformative impact on its market analysis. The precise data not only resolved their data-sourcing challenge but also elevated strategic decision-making in the Middle East and Africa.
FMCG · Global
POI
Car Traffic
Parcel
PIF
Govt. Riyadh
xMap delivered comprehensive city-scale motion data for PIF, leveraging geostatistical and demographic data.
By providing PIF with comprehensive city-scale motion data in Riyadh, xMap's solution not only overcame the challenge of swift access to diverse areas but also empowered PIF to enhance their decision-making with geostatistical insights.
parcel
Football
Demographics
Sovereign Wealth Fund
Lavazza
Coffee & F&B
To support its market entry strategy, the brand collaborated with xMap’s data solutions to transform its site selection strategy.
"By visualizing xMap’s data on maps, Lavazza could spatially understand market dynamics, leading to better decision-making."
Football
POI
Car Traffic
Sovereign Wealth Fund
Coca‑Cola
Japan Beverages
xMap addressed Coca-Cola Japan's challenge by providing extensive data access and availability beyond borders, unlocking new possibilities for sales enablement.
Empowered by xMap's solution, Coca-Cola Japan expanded its reach beyond the local market, tapping into comprehensive global data. This helped in developing sales enablement strategies, driving enhanced market insights and opportunities beyond Japan.
Car Traffic
Parcel
Demographics
Sovereign Wealth Fund
12 / Coverage
Live in 100+ countries.
Choose a coverage layer - the map highlights where that surface is strongest. Hover a highlighted country for depth.
13 / Developer Experience
Built for developers.
Documentation that reads like a textbook. SDKs in five languages. SLAs that hold.
01
First-class agent integrations.
MCP server pre-configured for Claude, Cursor, and any MCP-compatible agent. Tool definitions, schemas, and worked examples ready to paste. First query in under 60 seconds.
02
Five SDKs, one shape.
Python, TypeScript, Go, Rust, and Swift. All identical surface area. Same method names, same response shapes, same error types. Learn once, use anywhere.
03
Production-grade SLA.
99.95% uptime SLA, p95 query latency under 200ms, audit logs, SOC 2 Type II. Versioned snapshots mean your agent always queries a consistent state.
all systems operational · 99.97% uptime last 30d
14 / Pricing
Pricing built for builders.
Start on free credits, graduate to Pro with top-ups and auto-reload, or run unlimited throughput on Enterprise.
Developer
Explore every layer on real queries - no card required.
Free
- 1,000 credits included
- 5 requests per second
- REST, SDKs, and MCP
- Community support
15 / FAQ
Questions worth answering up front.
Is xMap only focused on the United States?
No. xMap is a global geospatial intelligence platform with POI and mobility data coverage across 200+ countries. We support both mature and emerging markets, allowing organizations to analyze commercial activity, movement patterns, and location intelligence at global scale.
What types of global data does xMap provide?
xMap provides global POI data, mobility data, traffic analytics, demographics, administrative boundaries, and enrichment layers across 200+ countries. Available attributes and granularity vary depending on the market and dataset.
How do retail and restaurant brands use xMap?
Retail and restaurant brands use xMap to evaluate new locations, analyze competitor density, understand foot traffic patterns, and identify underserved markets for expansion.
How do consulting firms use xMap?
Consulting firms use xMap to support market studies, location strategy projects, competitive analysis, and client-facing reports powered by verified geospatial data.
How do investment firms use xMap?
Investment teams use xMap to evaluate brand footprints, analyze expansion trends, benchmark competitors, and support commercial due diligence with real-world location intelligence.
What types of custom projects does xMap support?
xMap supports a wide range of custom projects including site selection, revenue forecasting, cannibalization analysis, market sizing, trade area analysis, mobility studies, territory optimization, competitor benchmarking, and expansion strategy projects.
What's the difference between xMap and Google Maps / Mapbox?
Google Maps and Mapbox are built for displaying maps to human eyes - navigation, tiles, embeds. xMap is built for machines. Every layer ships with agent-ready schemas, tool definitions, and MCP bindings. There are no tiles, no SDKs for rendering, no visual map component. It's a data infrastructure layer, not a mapping product.
How is xMap pricing structured?
We meter usage in credits. Developer includes 1,000 credits at no cost. Pro starts at $99/mo with credit top-ups and auto-reload, and scales to 25 requests per second. Enterprise starts at 1M+ credits per month with unlimited queries per second, VPC or on-prem options, and custom SLAs. Every tier includes the full data stack your agents need.
What's the data freshness across the layers?
POI data refreshes daily. Car traffic and GPS mobility update hourly. Parcel ownership and parcel price update weekly (or on public record filing for ownership). Demographics update monthly from aggregated panel data. Every snapshot is version-controlled - you can always query a specific date.
Where does the data come from, and is it licensed for commercial use?
xMap aggregates from satellite imagery providers, anonymized mobile SDK panels, public land records, commercial data partners, and government open-data sources. All layers are licensed for commercial use. Our mobility data is privacy-preserving by design - no PII, no individual-level tracking, all data is aggregated and anonymized at source.
How does the MCP integration work with Claude or other agents?
Run 'npx @xmap/mcp-server' and point Claude Desktop (or any MCP-compatible client) at it. The server exposes all 10 layers as named MCP tools. Your agent discovers them automatically - no routing code, no schema definitions to write. See our docs for a working Claude config in under 5 minutes.
What are the rate limits and SLAs?
Developer is capped at 5 requests per second. Pro supports 25 requests per second with production SLAs. Enterprise removes the ceiling with unlimited queries per second and limits tailored to your contract. Latency and uptime targets are agreed with your team on Pro and Enterprise.
Do you offer on-prem or VPC deployments?
Yes - Enterprise plans include VPC deployment on AWS, GCP, or Azure. We also support air-gapped deployments for regulated industries (banking, insurance, government). Contact sales for a scoping call.
How does xMap handle privacy and PII for mobility data?
All mobility data is aggregated and anonymized before it enters xMap. We never store or expose individual device IDs, individual trajectories, or any PII. Aggregates require a minimum cohort size before they're surfaced. xMap is GDPR-compliant, and our mobility data processing is certified under relevant privacy frameworks in each jurisdiction.
16 / Manifesto
We believe agents need a shared map of the world.
The first AI revolution gave machines a way to read text. The next one will give them a way to act in the world. But action requires grounding - a model of where things are, who owns them, who lives there, who passes through, what's nearby, what's changing.
That model has never existed in a form an agent can use. Maps were built for human eyes. Databases were built for human queries. Dashboards were built for human attention. None of them were built for a system that thinks at machine speed and operates at machine scale.
xMap is that model. We are building the canonical, queryable, live archive of the physical world - and we're making it the easiest layer in the agent stack to integrate.
That model has never existed in a form an agent can use. Maps were built for human eyes. Databases were built for human queries. Dashboards were built for human attention. None of them were built for a system that thinks at machine speed and operates at machine scale.
xMap is that model. We are building the canonical, queryable, live archive of the physical world - and we're making it the easiest layer in the agent stack to integrate.
- the xMap team · USA · 2026
// Blog & News
From the xMap team.
All posts →
Global
Footfall Traffic Data: A Complete Guide for Data-Driven Site Intelligence
Unlock the power of footfall data for smarter decisions. How can xMap transform your business with actionable insights? Discover the future of location intelligence.
February 10, 2026
20 mins read
Global
小売事業拡大 2.0: AI と地理空間データを活用して、よりスマートなサイト選択を実現
AI と地理空間分析が小売業の拡大をどのように変革するかをご覧ください。これらのツールは上位店舗を予測し、事業成長のリスクを軽減できるでしょうか？
February 28, 2025
読了時間：12 分
Global
電気自動車充電ステーションへの投資:総合ガイド
この記事では、これらの重要な側面について説明し、情報に基づいた意思決定を行い、輸送の将来を活用するためのデータ主導型の洞察を提供します。
December 12, 2024
読了時間：10 分
17 / In the press
xMap、バルセロナのスマートシティエキスポワールドコングレス2025でジェネレーティブAIマッピングプラットフォームを紹介
バルセロナ、スペイン
xMap takes the Stage at Google's MiddleEast HeadQuarter to Share 2025 Vision
Google HeadQuarter, Dubai, United Arab Emirates