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.

The physical world is a new frontier for AI, but modern AI-driven tools are limited by what they can do with this type of data. LLMs don’t understand how to use physical world data for analytical purposes. For example, when we asked ChatGPT to compute the flood risk from sea level rise for all homes along the California coastline, it came back claiming that “no one can give you a single exact number”, plus some unverifiable numbers:

That may have been true, until now!

In the past, it would take highly skilled developers who are familiar with spatial data and its query patterns, weeks to prototype this type of analysis. Now, developers and analysts, irrespective of their geospatial skills, can build it on-demand using natural language. Using your AI harnessed with Wherobots, they can build working solutions with small to very large scale geospatial data in minutes.

Wherobots now offers your AI the capability to understand spatial data to generate quality code. The pairing includes direct access to WherobotsDB, a cloud-based, secure, and distributed execution environment purpose-built to generate results efficiently at scale. Soon, the same AI will be capable of driving RasterFlow, the planetary-scale inference engine for Earth Intelligence to extract machine-generated insights from satellite, drone, and sensor datasets.

Enabling AI to Work with Physical World Data

Today we are launching three new tools that let AI understand and drive the analysis of spatial data; an MCP server, a CLI, and a VS Code extension. These tools are designed to bring geospatial development into IDEs like VS Code, Claude Code, OpenCode, Cursor, Windsurf, Kiro and others. And soon the Wherobots connector will make it easy to use the same analytical capability in Claude and ChatGPT from respective marketplaces.

With these tools, LLMs and agents can discover and understand spatial data, generate and debug code, and build solutions considerably faster using natural language as an interface.

As a result, developers and analysts immediately become more productive with geospatial data, enabling them to solve more problems, and shorten the development cycle from weeks down to potentially minutes. Wherobots integrates with data lakes and lakehouses including AWS S3, Databricks Unity Catalog, and AWS Glue, allowing teams to realize massive gains using their existing data.

Getting Started

1. Create a Wherobots Professional organization. Use Wherobots for free for 30 days and up to $300.
2. Create a Wherobots API key.
3. Install the Wherobots extension for VS Code.

Optionally, establish secure integrations with datasets in Amazon S3, Databricks Unity Catalog, or AWS Glue. Wherobots will utilize these integrated datasets, along with hosted datasets within its catalog.

You can also install Wherobots on other IDEs, work with the MCP server, utilize the CLI and agent skills.

Start using the extension by typing a prompt into the chat window:

• “Using NDVI datasets and field boundaries for California, what kind of field-level crop health insights can I compute?”
  
• “Write a notebook for me to ingest the latest American Community Survey data from US Census.”
  
• “I need to know how many properties in California coastal cities are facing flooding risk. I want you to come up with an analysis framework for this, help me identify the right datasets to help me solve this problem, generate some initial insights from those for me to validate, and finally (once I approve) write a notebook for me to run this analysis independently.”

Using VS Code and Claude Opus 4.6 harnessed to Wherobots, we were able to complete the California flood risk analysis that ChatGPT couldn’t in under 30 minutes and for less than $5 of Wherobots usage. The California Coastal Flood Risk notebook we built is here.

Here is a quick video interacting with a generated notebook via VSCode.

Enabling Agents to Understand the Physical World

What we announced today enables people to use AI to build solutions with physical world data, at a fraction of the time and cost. Agents are now capable of developing production-grade geospatial data applications, autonomously or semi-autonomously with a human in the loop with Wherobots acting as the natural language interface and the context engine between the two.

What’s Possible Now

Here are a few example prompts that can drive prototypes and working solutions. To build prototypes or solutions, you will need to ensure the right data is integrated with Wherobots such that your AI can use Wherobots to understand it, and execute effectively based on your directions. For many organizations, this data is already available either in their private data lake (Amazon S3, Databricks Unity Catalog, AWS Glue Data Catalog) or in the public domain (STAC, public datasets, purchased datasets).

Mobility, fleet management, and logistics: “Use Wherobots to transform the raw GPS data for the month of March [located in Databricks Unity Catalog Table X] into trips, and match it to the Overture transportation network using Wherobots map matching. Tell me which segments of road in the state of California were the most constraining for my trips. Define constrained as the speed traveled was less than 50% of the advertised speed limit, rank these segments by trips taken, and also eliminate road segments that were within 1/4 mile of an intersection.”

Marketing and advertising: “Using the fields of the world dataset generated by Wherobots RasterFlow, join fields to Regrid parcels. Also join this result with my customer database located in [S3 location]. I want to identify unique farm owners who are not customers and sell to them.”

Insurance: “Compute the flood risk score using the [flood plane raster] for all properties under general home insurance located in my [S3 bucket path]. Identify which properties have flood insurance and are at the most risk, based on [criteria X]. Separately tell me which properties are at risk, but not insured so I can target them for insurance offerings.”

Agriculture: “Use the fields of the world dataset as a filter. Use RasterFlow and the latest Sentinel 2 data in the AWS data exchange to compute NDVI for the state of California. Join these results with the fields to produce NDVI statistics in the month of July for all fields in California.” (This example will be AI-driven soon, but is feasible today with RasterFlow in private preview)

What’s Coming Next

In the coming months you can expect:

• Wherobots Connector on the Claude and OpenAI Marketplaces
• OAuth support for the MCP Server
• Additional hosted datasets in the Wherobots Hub
• New integrations with additional data sources and catalogs

Here’s a sneak peek of the experience we are planning to enable via Claude, including notebook generation and insights provided directly inside Claude Chat:

Please reach out to us at product@wherobots.com or support@wherobots.com if you have feedback, requests, or questions.

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