HERE Technologies and Wherobots Partner to Give AI Agents Geospatial Intelligence
SAN FRANCISCO, CA / ACCESS Newswire / October 14, 2026 / Wherobots, and HERE Technologies, the world's leading mapping and location data company, have formed a strategic partnership to enable AI agents to understand and make precise, deterministic decisions about the physical world.
Initially, Wherobots and HERE are integrating HERE Location Reasoning (HLR) with the Wherobots MCP (Model Context Protocol) server. Customers of this integrated solution can now build AI agents that combine complex and large-scale physical world analysis with precise geospatial intelligence in a single workflow. These include use cases such as last-mile delivery and logistics planning, infrastructure risk computation, dispatch design and resource allocation, site selection, property and casualty underwriting, and more.
AI has the potential to positively transform how and where assets are allocated and move. But to achieve this, AI must be capable of effectively utilizing the data about the physical world. Currently, AI agents heavily rely on external tools to generate the context required to make decisions, but, because of the preference of AI models to select free and open tools that they can easily harness, the teams that utilize them frequently start with solutions that seem initially robust, but are often flawed. They can't analyze the data across spatial modes at scale, and often require bespoke and complex builds to deliver answers that are only partially reliable.
Together, Wherobots and HERE bring the tools designed to enable AI agents to understand the physical world through large, diverse datasets and deterministic guidance, enabling a new tier of optimization and innovation. Through an MCP integration, AI agents turn on high-performance inference and analytics on multiple types of spatial and non spatial data at various scales within Wherobots, and join this with precise geospatial intelligence offered by HLR. The result is a high-value agentic workflow, with the capability and precision that can unblock innovation and resource optimization across industries and use cases.
Quotes
"AI is moving from generating content to making decisions and taking action in the physical world. That requires more than a language model. It requires a trusted understanding of where things are, what rules and conditions apply, and what actions are possible in the physical world. The partnership between HERE and Wherobots gives developers a simple way to bring that geospatial intelligence into the systems they are already building."
- Mahesh Shah, Senior Vice President of Enterprise & AI Solutions at HERE Technologies
"Agents increasingly answer questions, complete tasks, and run workflows on our behalf, and many of those tasks require deterministic answers about the physical world. Adding HERE Location Reasoning to the Wherobots context engine for physical world data gives people and agents that precision in one workflow. Leading technology and data providers have a responsibility to make that possible."
- Mo Sarwat, CEO, Wherobots
Why we're building this
Wherobots and HERE have been working on this problem from complementary directions for some time. Wherobots processes satellite imagery, mobility, weather, risk and operational data at regional to planetary scale to produce forecasts, scores and enriched datasets. HERE Location Reasoning provides AI-native access to trusted HERE geospatial intelligence, including live traffic, geocoding, place discovery, passenger and commercial vehicle route optimization and EV charging intelligence.
The future of agentic workflows need both. The partnership between the two companies makes it easy for agents to process large scale and historical physical world datasets, as well as inform decisions with live, on the ground intelligence, so that AI can accelerate and increase the accuracy of their location-aware development cycles.
How it works
An AI agent connects to one endpoint, the Wherobots MCP server. Wherobots sends each sub-task to the engine built for it:
What agents can do
Each workflow below runs as a live demo app in the Havasu Knowledge Lake.
Outputs can be delivered to any cloud storage or lakehouse.
These examples show how the Wherobots integration with HLR can support broader real-world AI use cases across logistics, infrastructure, insurance, site selection, mobility, field operations asset deployment and customer support, where AI systems need reliable spatial context at scale, with precise intelligence to support operational decisions.
Availability
Within Wherobots integration with, HLR is available through Wherobots Spatial AI Tools and MCP Server, with support for the Wherobots first-party agent coming soon. To explore the integration, documentation, examples and reference workflows, visit wherobots.com.
About Wherobots
Wherobots is the context engine for physical world data. It is the data and infrastructure layer that enables processing of small to large scale physical world data, geometries such as buildings, waterways, road networks, as well as imagery from satellites, drones or airplanes. Existing AI infrastructure was trained on text, code, and traditional database tooling, but the most consequential enterprise decisions often deal with assets and infrastructure in the physical world: supply chains, properties, geopolitical risk, and infrastructure.
Built by the original creators of Apache Sedona, Wherobots lets teams process imagery at planetary scale, run precise spatial joins across billions of geometries, deploy computer vision models on earth data, and give AI agents persistent memory of everything their organization has built within the physical world.
Apache Sedona and Wherobots are used by some of the most forward thinking companies to optimize their products in the realities of the physical world and stay ahead of an evolving planet including: Amazon, Nearmap, Ordnance Survey, Allstate, Uber, T-Mobile, Zillow, Oak Ridge National Laboratory, J.B. Hunt, Land O' Lakes, and more.
Contact:
Lindy PR
[email protected]
SOURCE: Wherobots