Geospatial Platform Modernization at Scale

NEO struggled to scale its geospatial platform as growing demand outpaced engineering capacity and limited non-technical user access to data insights.

The Challenge

Scaling Geospatial Intelligence with a Custom Platform for NEO

As organizations increasingly rely on earth observation data for risk management and strategic decision-making, the challenge lies in transforming raw satellite imagery and geospatial data into actionable insights, without requiring deep technical expertise.

Our partner NEO, a leader in geospatial intelligence, has built a robust data platform called SignalEyes, which processes terabytes of satellite imagery, aerial photography, and geospatial data. Their clients, such as municipalities, use this intelligence to inform urban planning, assess green space accessibility, and improve overall quality of life.

To move beyond custom, one-off analyses that relied heavily on engineering time, NEO set out to make their data and services more scalable and accessible to non-technical decision makers. That’s where we came in.

We helped NEO evolve SignalEyes into a more flexible, reusable product architecture built for growth. By modernizing their data delivery pipeline and enabling more self-service capabilities, we empowered NEO to scale their insights across more use cases, with less technical overhead.

The Solution

Making Geospatial Intelligence Scalable with the SignalEyes API

To help NEO make earth observation data more accessible and scalable, we built the SignalEyes API, a dynamic, GenAI-powered self-service platform. Users can interact through a natural language interface, select geodata sources, zoom into areas of interest, and run analyses using a library of preconfigured concepts.

For example, users can count trees within a set radius of buildings, applying custom filters like height or distance. The backend, built with FastAPI, AWS Cognito, OpenStreetMap, and multiple geospatial databases, supports data on trees, buildings, green spaces, and solar panels.

SignalEyes is designed for growth, allowing new data sources and analytical concepts to be added over time without reengineering. To support reusability, we introduced SignalEyes API Analyses, ready-to-run models like the 3-30-300 rule, used by cities to assess tree coverage and green space access.

The API supports three access points: a web interface, a developer API, and a GenAI chat interface making advanced geospatial analysis available to both technical teams and non-experts alike.

The results

Accelerating Geospatial Insight Through GenAI

The application is brand new, and we are in the early phase of proving the value in practice. However, we hear that users from different types of organizations expect to analyze geospatial data faster, cheaper, and more dynamic. The intuitive setup of the application, and the GenAI interface enables users with less technical knowledge to perform any analysis they have in mind, without the help of an expert.
 
Furthermore, technical domain experts at NEO can now focus on adding new data sources or extending the library of preconfigured analyses, instead of executing custom user requests in a manual fashion. Altogether, an easier, more scalable, and more reusable way of working.

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