We build AI models for the European Space Agency ESA in various domains such as Navigation (Navisp) and Earth Observation (EO). We use AI built on space assets to determine the best locations for advertising.
BeSpatial.AI helps businesses in financial services and other industries to boost their ads performance with customer trained machine learning model (HolistiCRM).
Training is done based on internal datasets, Earth Observation provided by European Space Agency ESA and GIS data (GeoX). In our first use-case machine learning model accuracy reached 91%.
Machine Learning supported targeting ROI reached 1127% thanks to lower CPC and higher conversion rates targeting the right (potential customer dense) locations.
BeSpatialAI is a B2B service. We are creating value for companies in the financial services and retail sectors through making their digital advertising efforts more efficient. Based on discussion with customers, the direct users of our product will be the departments responsible for sales and marketing across digital channels.
Advertisers are looking for tools to better target their messaging to reach their customers. The current best practice in digital campaign management is to train a new model for each product offering, however this is a time and resource-consuming process and uses internal data that is disconnected from the Facebook/Google ad systems.
Companies often run many parallel digital campaigns without a machine learning component to optimize performance using both internal knowledge and geospatial information. There is widespread recognition that better integration and utilization of data analytics can be a real competitive advantage.
BeSpatial.AI enhances targeting accuracy based on geolocational similarity and customer affinity maps. It reduces the cost and the resources required to generate a new, specific model for a target group because its automated MLOPS process doesn’t require man months of scarce internal resources (IT, BI, CRM) involvement.
Navigation (ESA NAVISP)
We have developed a learning solution to model the EGNOS ground-segment processing computations. The machine learning model predicts EGNOS performance in terms of predicted ionospheric and orbit and clock models (GIVE and UDRE), which can then be translated to predicted protection levels that are important for aviation applications and possible other safety-of-life uses. The project was coordinated by Integricom, HolistiCRM developed the AI model, Iguassu developed the interfaces to EGNOS and UI, GEOX developed extract transfer load modules.
