Real-time Analytics
Xoople needed to turn massive volumes of satellite and geospatial data into enterprise-ready AI assets that customers in industries such as agriculture, logistics and insurance could actually consume inside their workflows, while also addressing market fit, privacy and trust.
Reported impact
Xoople gained a stronger commercial and technical foundation for taking Earth-data intelligence to market, enabling customers to consume predictive geospatial insight more easily while trusting the privacy and security of the underlying data platform.
Xoople and EY expanded Earth observation data into a commercial AI platform by working across market design, data engineering and trust. EY first helped validate target industries and create go-to-market playbooks explaining how Earth data intelligence could be integrated into enterprise use cases. On the technical side, the joint team transformed complex geospatial inputs into AI-ready data sets using multimodal AI and Generative AI techniques, while working with hyperscaler collaborators including Microsoft and Databricks to make those data sets consumable across different enterprise architectures. Open, platform-agnostic data formats were created so customers could adapt the outputs within their own stacks, and the program added zero-trust security, least-privilege access controls and continuous verification to make the data trustworthy. The result was an Earth-data intelligence foundation that can deliver predictive business insights rather than raw satellite imagery.
Satellite and geospatial inputs are collected into Xoople's Earth data intelligence pipeline, where multimodal AI and Generative AI help convert them into AI-ready data sets. Those outputs are packaged in open data formats, secured with zero-trust controls and then integrated into customer workflows so end users receive predictive business insight instead of raw Earth observation data.
"EY and Xoople tapped into an ecosystem of hyper-scaler collaborators, including Microsoft and Databricks."
"One innovative approach that was necessary ... was to create open data formats that were platform-agnostic."
"We took a zero-trust approach. With this, we applied least privileged principles. We implemented user-based access controls and continuous verification."
"Xoople makes incomprehensible amounts of Earth data comprehensible by leveraging multimodal AI."
"Xoople's prototypes are fueled by petabytes of data (soon to be exabytes)."
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Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
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