Wherobots is Bringing Spatial Context to AI
Wherobots is launching a set of features designed to make its powerful geospatial processing capabilities accessible to modern AI systems.
Building on its core compute engine — the Wherobots DB, which processes two-dimensional data like map and trip information; and its raster flow tool, which handles aerial imagery data from satellites and drones — Wherobots is making these capabilities accessible to AI.
Wherobots is positioning its enhanced product as the “spatial context engine,” serving as a source of spatial context for these AI systems. The new layer allows users to interact with Wherobots’ integrated data using natural language.
“Imagine you have this theory about something in your business that’s related to the physical world,” Damian Wylie, Wherobots head of product, explained to SD Times. “Just an intelligence question around risk, like, ‘What assets in my portfolio are most likely to be at risk of floods or climate rising sea levels?’ You can ask that question to an AI agent, and you’ll get that result based on the use of Wherobots and all the data that Whereobots is integrated with.”
Wherobots already integrates not only open data sources, such as satellite imagery, but also customers’ proprietary data like business assets and trip data located in Amazon S3 buckets.
A key benefit is the simplification of working with spatial data, a task often foreign to many developers, Wylie said. Developers no longer need to worry about spatial data formats or complex spatial queries; they only need to focus on framing the question. While customers are not expected to use the generated code as-is in production, they gain the ability to “test the ideas much faster” and reduce one of the most costly elements of working with spatial data—code development.
The need for highly accessible spatial data extends to anyone investing in the physical world at scale. The use cases, Wylie said, are widespread across multiple commercial sectors:
- Delivery and Logistics: Understanding how the dynamics of the physical world impact existing operations, future expansion, and last-mile delivery changes.
- Real Estate: Assessing risk from climate change or fire, and identifying investment properties that are likely to produce the highest returns.
- Government/Defense: Government agencies use it for change detection, such as identifying unpermitted development by running machine learning models on satellite imagery, all orchestrated by AI agents.
- Energy/Agriculture: Large-scale energy providers can determine the next best solar investment, and agriculture is cited as another obvious beneficiary.
This technological shift is fueling massive growth in the market. The broader geospatial market is projected to be between $200 and $400 billion, and commercial investment is expected to surpass the spending by governments and the military. This accelerated growth is supported by the fact that the technology is becoming more accessible to commercial organizations, making it look and feel like any other cloud engine.
Wylie mentioned that in the next release, the company will announce a plugin in the AWS Cloud Marketplace that users can avail themselves of and start asking questions in natural language. Wherobots will pull from what is publicly available – assuming an S3 bucket hasn’t yet been set up as an integration point – and start answering these questions with real data.
The post Wherobots is Bringing Spatial Context to AI appeared first on SD Times.
Tech Developers
No comments