Wherobots and Felt Partner to Modernize Spatial Intelligence

We’re excited to announce Wherobots and Felt are partnering to enable data teams to innovate with physical world data and move beyond legacy GIS, using the modern spatial intelligence stack.

The stack with Wherobots and Felt provides a cloud-native, spatial processing and collaborative mapping solution that accelerates innovation and time-to-insight across an organization.

• Wherobots delivers the most capable spatial query and inference engine for creating insights from physical world data (raster, vector, structured) of any scale. 
• Felt delivers an intuitive, browser-based experience that enables business teams to explore this data, ask questions, and share insights collaboratively.
• The combination provides a new path to move forward for teams that are innovation-limited by their desktop-bound GIS tools, restrictive licensing arrangements, and unscalable workflows.

What is Felt?

Felt is the new standard for collaborative mapping, and their product is often described as “the Google Docs of GIS.” It is a cloud-native platform designed to turn complex geospatial data into actionable insights through its unique collaborative map development capabilities. Unlike traditional Geographic Information Systems (GIS) that are often desktop-bound, Felt lives entirely in the browser, allowing teams to create, analyze, and share interactive maps with the speed and ease of a modern productivity tool.

Why Legacy GIS is Limiting Spatial Data Innovation

Spatial data comes in various formats and sizes, and it needs to be processed and combined with other datasets for people, systems, and AI to innovate with it. Because few systems were developed to handle this wide spectrum of data complexity, format, and scale in a graceful way, users have been forced to create cumbersome, time consuming workarounds. In turn, this has led to a high degree of specialization, and a special category of GIS tools that simply can’t keep up with demand.

Buyers, faced with few options, have been limited to old-guard licensing arrangements for lagging technology solutions. Such engagements plant a thorn in the side of many organizations who end up constrained in their ability to innovate unless they hire more specialized staff or contractors to develop workarounds that copy data to and from bespoke GIS tools or databases. What buyers want is to deploy modern tooling and AI that “just absorbs the complexity” such that their data practitioners can just work with this data and achieve their goals. They also want more flexibility to take the best tools over time and demand data sovereignty.

If you’re working on a small yet nimble team of data scientists and engineers solving world-level problems, you want the capability to easily crunch terabytes of vector, raster, and structured data using familiar tools. Data interactivity is key because the easiest way to understand spatial data quality at scale is to inspect relationships visually with performance, and you can iterate faster towards the shared objective when in-team collaboration is seamless. Historically teams relied on piecemeal operations that extended the innovation cycles because of data and work siloes, or limited tooling support. 

Similarly, analysts, planners, and stakeholders want simple, AI enabled, visual-first tools to understand and ask their questions about spatial data so they can make decisions from it. Their ability to tailor analytics from a visual-first tool, was limited by the capabilities of the underlying query engine and the data available to it.

Moving from Legacy GIS to a Modern Spatial Intelligence Stack

Our partnership with Felt directly addresses this friction with a seamless integration between platforms to deliver the spatial intelligence stack. 

Using Felt’s AI-assisted and collaborative map development experience, customers can easily query, interrogate, and build insightful visualizations from multiple sources of spatial and non spatial data in their private data lakes or open repositories. This is done using Wherobots as the query engine that absorbs the heavy lifting associated with multi-modal spatial data processing and cataloging. 

Key Benefits of the Wherobots and Felt Integration

• Easy for everyone: No user is left behind. Wherobots supports data engineers, scientists, and developers who need speed and performance, while Felt enables front end developers, GIS users, and business teams to get insight, instantly from the analysis.
• AI first: from inference, MCP tools, to collaborative maps driven by AI and natural language prompts, users have the modern AI tools to generate insights quickly at scale. 
• Built for the lakehouse: The lakehouse architecture maximizes data sovereignty and agility by giving you the freedom to choose the best tools for the job over time. 
• Spatially optimized: World class spatial capability and performance is ready out of the box. Wherobots provides the scale, performance, and capability to crunch and perform inference on multi-modal spatial data than any other cloud platform. 
• A fully managed, friendly, open standards based deployment: on-demand deployment and pay as you go pricing lets you reach your goals through open, well understood standards and architecture.

Customer Example: How Leaf Agriculture Uses Wherobots and Felt

The partnership started with a joint customer request from Leaf Agriculture, who has been using Wherobots and Felt for productionizing their LeafLake offering over this past year, and to harmonize large scale tractor data for their Leaf Unified API. Leaf serves customers and partners like Syngenta, Bayer and Farmers Edge, and many others in the agricultural economy. 

The delivery of LeafLake, supported by Wherobots and Felt, creates a high-velocity data fabric that turns fragmented agronomic data into planetary-scale intelligence. Wherobots serves as the query engine, running distributed spatial SQL operations to create insight-ready datasets from millions of acres of machine data and imagery at 5–20x the speed and at a fraction of the cost of traditional solutions. By running directly on Leaf’s unified data lake, Wherobots transforms raw telemetry into structured, “AI-ready” insights in seconds.

Felt acts as the collaborative “window” into this data, providing a high-performance mapping interface that lives entirely in the browser. Through a native integration, data processed in Wherobots is made directly to Felt. This “SQL-to-map” workflow allows agronomists and decision-makers to interact with LeafLake data in real-time in Felt, enabling a “Google Docs” style of collaboration over complex agricultural insights.

This map showcases farm data from Leaf Agriculture’s LeafLake platform through Felt’s interface for the purpose of precision farming based on tractor telemetry and soil data.

Get Started Today

This integration marks a major step forward making the creation of spatial intelligence accessible to entire organizations. We can’t wait to see what you build with the combined power of Wherobots and Felt. 

To learn more: 

• Check out the documentation to get started in minutes.
• Join us in this upcoming session to see how Wherobots and Felt work together to build the foundation for spatial AI.
• Talk to our team to explore options.