Introducing developer tools that let AI build with physical world data

Your AI can now understand and query spatial data using the Wherobots MCP server, VS Code extension, and CLI.

It takes 15 minutes for the Caltrain to get from Sunnyvale to SAP Center

That’s how long it took our MCP server to go from “how many bus stops are in Maryland” to an answer

Scaling Spatial Analysis: How KNN Solves the Spatial Density Problem for Large-Scale Proximity Analysis

How we processed 44 million geometries across 5 US states by solving the spatial density problem that breaks traditional spatial proximity analysis

How Aarden.ai Scaled Spatial Intelligence 300× Faster for Land Investments with Wherobots

When Aarden.ai emerged from stealth recently with $4M in funding to “empower landowners in data center and renewable energy deals,” the company joined a new wave of data and AI startups reimagining how physical-world data drives modern business. Their mission: help institutional land investors rapidly evaluate the value and  potential uses of land across the country. […]

Raster Spatial Joins at Scale: Google Earth Engine and BigQuery vs Apache Sedona and Wherobots

Perform spatial joins at scale and zonal statistics with vector and raster data using Google Earth Engine & BigQuery vs. Apache Sedona & Wherobots. Compare performance, architecture, and geospatial for geospatial analysis.

Dekart Supports Wherobots as a Spatial SQL Engine

With Dekart now supporting Wherobots as a Spatial SQL engine, this combination creates a snappy, highly scalable query visualization experience for spatial data in your lakehouse.