How Xoople transforms Earth data into business insights

While designing Earth observation missions from satellite data at the end of the 20th century, Fabrizio Pirondini saw the landscape begin to change. Once the exclusive domain of governments, this industry, born through satellite launches, was opening up to the private sector thanks to technology improvements and regulatory changes.

Pirondini saw this as an opportunity to push the industry forward. As the co-founder and CEO of Xoople, he is now leading a team that enables businesses to access insights about changes on the Earth’s surface through advanced AI and analytics.

Pirondini calls this Earth data intelligence. “At Xoople, we’re removing all the complexity of having to search for, collect, process and make sense of this huge amount of geospatial data,” he says. “Earth data intelligence does more than just capture data about changes happening on the ground, such as shifts in weather, traffic or shipping. It also extracts patterns and predictive insights from that same information.”

Imagine running an agriculture company and being able to track the status of each of your crops in real time: what they need to survive and even how they will grow over time. Or being in logistics and knowing what’s happening with key nodes in your supply chain instantly, whether it’s a natural disaster or disrupted road. Or being an insurer and monitoring your assets globally and how they are performing. Xoople’s prototypes are fueled by petabytes of data (soon to be exabytes) that allow it to feed users with a regular flow of new information that can track and predict these changes.

While Xoople creates the data sources and makes sense of them with the right tools, enabling seamless adoption by end users is vital. “The last mile of the delivery of our solutions is key,” Pirondini says. “They have to be integrated into the enterprise workflow and built around real-world needs.”

Xoople is delivering upon this mission. And they have teamed with Ernst & Young LLP, leveraging their strategic consultancy capabilities to help turn their solutions into commercial blockbusters that businesses around the world will adopt to make better, faster and more informed decisions.

At the beginning of its engagement with EY, Xoople’s data was envisioned to enable remote sensing through geographic information systems. “It’s a very technical type of field, and as we dug in we realized there is so much more use for this data,” says Traci Gusher, EY Americas AI and Data Leader. “So many industries can benefit and drive value from it — it’s really amazing.”

Together, Xoople and EY tackled three key challenges.

1. Research and validate the market fit and go-to-market strategies

The project required engagement from infrastructure, data, AI and software engineers and specific industry domain professionals across the EY organization to help determine how the market fit of these data sets could create value. Together, they developed go-to-market strategies and programs for clients, including playbooks to explain the insights derived from these data sets as well as how to integrate them at an enterprise level.

2. Technically transform complex geospatial data into AI-ready data sets

Generative AI (Gen AI) was not a mainstream technology at the start, forcing the EY team to pivot again from the original impetus of the engagement. “We realized that the acceleration of the availability of this data was going to become increasingly important,” Gusher says.

To understand how to better enable AI to deliver fast results for commercial enterprises, EY and Xoople tapped into an ecosystem of hyper-scaler collaborators, including Microsoft and Databricks. These efforts were core to creating the distinctive data sets as well as facilitating them for enterprise.

“One innovative approach that was necessary as EY and Xoople teamed together was to create open data formats that were platform-agnostic,” Gusher notes. “The use cases are cross-industry, and companies utilize various technologies, so it was important to have data sets that are in open formats that can be consumed and customized by different architectures.”

3. Enable privacy and security in the solutions

“Xoople must deliver not only an effective solution but a trustworthy solution,” Pirondini says. “We’re working with technological partners on a unified global data approach that has an added extra layer of cybersecurity. This allows us to be sure that we have secure and efficient access to our data, and our customers can trust that their data is secure,” he adds.

Robust risk governance is a core component of AI-ready data, and EY teams put Xoople’s data set through extensive testing and enablement, to help ensure it was not only usable and accessible, but also trusted. “Building trust into this data set was absolutely an imperative and foundational part of this program,” Gusher says. “We took a zero-trust approach. With this, we applied least privileged principles. We implemented user-based access controls and continuous verification to help ensure the data was secure and could be trusted.”

Xoople’s comprehensive approach — considering AI from how it’s marketed, how it’s enabled and how it’s secured — aligned with how EY approaches the technology through its EY.ai Value Blueprints. These blueprints consist of seven layers, such as intelligence, trust and customers, so that AI is built into the future, not bolted on in the past.