How Wherobots builds with NVIDIA to let AI see the physical world

The world and what happens in it is digitized by petabytes of raw and derivative spatial datasets of various data types and scales, and the potential for applying AI to it is immense. But the AI models and agents we use every day need connectivity to tools that turn this data into usable insights and relationships. Wherobots gives AI the ability operate on and understand raw physical-world data, and NVIDIA GPUs are core to it. Architecturally here’s how this works at a high level.

RasterFlow is now available in Public Preview

RasterFlow makes planetary-scale earth intelligence workflows easy and costs predictable. We are excited to announce that RasterFlow is now in Public Preview, opening up the power of planetary scale Earth Intelligence to all Wherobots Professional Edition customers! RasterFlow let’s you solve complex monitoring challenges with vision-language models or tailored models for specific use cases, without […]

The Wherobots Spatial AI Assistant is now in the Anthropic Connectors Directory

You can now ask Claude questions about the physical world and get answers grounded in real spatial data.  The Wherobots Spatial AI Assistant, now available in the Anthropic Connectors Directory, answers these questions in plain language and returns results, maps, and reports directly in your Claude conversation. Wherobots is the AI context engine for the […]

Spatial Graph RAG for the Physical World

Introduction RAG (Retrieval Augmented Generation) has addressed one of AI’s biggest challenges for enterprise users: missing or hallucinating empirical business and real world context . Instead of generating answers from nothing, RAG retrieves relevant documents and feeds them to the model as context. It works. Ask an AI about your company’s Q4 revenue, and RAG […]

How well does SAM3 detect building footprints? We asked the Wherobots Spatial AI Coding Assistant

In a recent post, we showed how easy it is to use RasterFlow and Meta’s Segment Anything 3 Model (SAM3) to detect features in the physical world. A single end-to-end pipeline built a 133 GB NAIP mosaic of Marion County, Oregon, ran SAM3 against it with text prompts spanning eight classes, and produced approximately one […]

Wherobots MCP Server: Building GEOINT Spatial Pipelines with AI Agents

Editor’s note: The Wherobots Spatial Data Catalog is now the Havasu Catalog. I built three national-security GEOINT use cases on the Wherobots stack in days instead of weeks. A Critical Infrastructure Vulnerability (CIV) pipeline with two regional variants, plus a border-corridor analysis on real transportation segments. The Wherobots geospatial MCP server is what made that […]

Detecting Objects From Text Prompts with RasterFlow and Segment Anything 3

Exploring the capabilities of Segment Anything 3 on high-resolution Earth observation data.