Stop scraping.
Start deciding where to build.
EV chargers, data centers, solar farms, coliving, coworking, self-storage — six globally-curated, weekly-refreshed datasets with US Census demographics. Three ways in: self-serve Atlas, custom Insights, or agent-native API.
- Score 1,000 sites for charger deployment
- Pick the next data center metro
- Find under-served coliving markets
All your location data,
one place
No more juggling census spreadsheets, Google Maps tabs, and expensive GIS tools. We bring it all together.
Every dataset you need
Demographics, traffic patterns, points of interest, infrastructure — layered on a single map. Stop copying data between 5 different tools.
Analysis without the GIS degree
Run trade area analysis, demographic breakdowns, and competitor mapping without learning ArcGIS. It just works.
Ask it anything
Type "show me coffee shops within 2 miles of downtown Austin" and get an answer. No queries, no filters — just ask.
Reports your team will read Coming Soon
Export clean PDFs for your investors, share a link with your ops team, or drop it into a board deck. Built for people who don't love spreadsheets.
Pick your entry point
Same data foundation, three different ways to use it.
Unearth Atlas
Self-serve location intelligence. Bring your data, ask questions in plain English, get AI-driven analysis combining your data with our 6 infrastructure datasets. Free tier + paid tiers from $49/mo.
- Free tier: 10 queries / 100 records
- Plain-English queries via AI chat
- BYO data + our 6 infrastructure datasets
- Same tier shape as Agents
Unearth Insights
Custom-built location intelligence for teams making real-estate, expansion, and operations decisions. Hands-on engagement scoped to your data and your decisions.
- BYO data — secure intake at any volume
- Custom dashboards, reports, exports
- Hands-on engagement from the Unearth team
- SLA-backed support, scheduled refresh
Unearth Agents
Agent-native API. One config line. Drop into Claude Desktop, Cursor, Cline, or any MCP-compatible agent. Live data, time-series diffs, machine-readable pricing.
- MCP server —
npx -y @unearth-ai/mcp - OpenAPI spec, JS + Python SDKs
- Free tier (100 calls/mo) — paid from $49
- Time-series snapshots since 2026-04
Who uses this
Retail & Franchise
Opening 10 new stores this year? See exactly where foot traffic, income levels, and competitor gaps line up.
Financial Services
Figure out which branches are underperforming and where the underserved markets are hiding.
Real Estate
Due diligence on a property used to take weeks. Now you can see surrounding demographics, traffic, and competition in minutes.
Logistics & Industrial
Place warehouses where they actually reduce delivery times. Plan routes that make sense, not just look good on paper.
What our users say
"We added 50 locations in the last 12 months. Seeing the demographic and customer segmentation data on a map has been super helpful for us to expand our territories more strategically."
"Unearth AI's software helped us understand our restaurants' trade areas and customer base in a way that was not possible before."
"Having all the demographic and traffic information in one place helped us to prioritize potential location partners a lot faster."
Explore Our Location Datasets
Curated global data on EV infrastructure, residential markets, and more — updated weekly and ready to drop into your analysis.
Global EV Charger Stations
Network operators, charge levels (DC Fast / Level 2 AC), port counts, power output, and precise coordinates — across 58 countries. Updated weekly.
Global Co-Living Properties
Monthly pricing, room types, amenities, minimum stay, and ratings for shared housing worldwide — ideal for PropTech and market research.
Global Coworking Spaces
Capacity, amenities, accessibility, and opening hours for coworking spaces worldwide — sourced from OpenStreetMap. Updated weekly.
Get notified when datasets refresh.
Weekly digest: new datasets, country coverage, methodology changes. No spam, unsubscribe in one click.
Three products. One data foundation.
Pick the surface that fits your team — or use all three. Same datasets, same weekly refresh, same time-series moat under all of them.

