Wherobots introduces a set of features designed to make its powerful geospatial processing capabilities accessible to modern AI systems.
Building on its core computing engine — Wherobots DB, which processes two-dimensional data such as maps and travel information; and its raster streaming tool, which handles aerial imagery data from satellites and drones – Wherobots make these capabilities accessible to AI.
Wherobots positions its advanced product as a “local content engine,” which serves as the source of local context for these AI systems. The new layer allows users to interact with Wherobots’ aggregated data using natural language.
“MeI think you have this idea about something in your business that has to do with the virtual world,” Damian Wylie, Wherobots’ head of product, explained to SD Times. “It’s just an intellectual question about risk, like, ‘What assets in my portfolio might be vulnerable to flooding or rising sea levels?’ You can ask that question to an AI agent, and it will get that result based on the use of Wheobots and all the data that Wheobots has compiled.”
Robots already integrate not only open data sources, such as satellite imagery, but also proprietary customer data such as business assets and travel data available in Amazon S3 buckets.
The main benefit is the ease of working with location data, a task that is often unfamiliar to many developers, Wylie said. Developers no longer have to worry about spatial data formats or complex spatial queries; they only need to focus on formulating the question. Although customers are not expected to use the generated code as it is produced, they gain the ability to “test ideas very quickly” and reduce one of the most expensive aspects of working with spatial data—code development.
The need for highly accessible location data extends to anyone investing in the virtual world at a high level. The use cases, Wylie said, are spread across many commercial sectors:
- Delivery and Arrangement: Understanding how global change is impacting existing operations, future expansion, and last-mile delivery changes.
- Real estate: Assessing the risk of climate change or fire, and identifying investment properties that are likely to generate high returns.
- Government/Defense: Government agencies use it for change detection, such as identifying unauthorized development by using machine learning models on satellite imagery, all programmed by AI agents.
- Energy/Agriculture: Large energy suppliers can determine the best solar investment, and agriculture is cited as another obvious beneficiary.
This technological change is fueling huge growth in the market. The broader geospatial market is expected to be between $200 and $400 billion, and commercial investment is expected to exceed government and military spending. This rapid growth is supported by the fact that the technology is easily accessible to commercial organizations, making it look and feel like any other cloud engine.
Wylie said in a subsequent release, the company will announce a plugin on the AWS Cloud Marketplace that users can use and start asking questions in natural language. Wherobots will pull from what’s publicly available – assuming an S3 bucket isn’t already set up as an aggregation point – and start answering these questions with real data.
