Wiliot Partners with Databricks to Expand Physical AI Capabilities in Supply Chains
Wiliot has entered into a strategic partnership with Databricks to run its Physical AI platform on Databricks’ data infrastructure. The collaboration focuses on enabling real-time data integration and analytics across supply chain operations.
Wiliot announced a partnership with Databricks to deploy its Physical AI platform and supply chain automation solutions on the Databricks platform. The collaboration positions Wiliot as a Databricks Built-On partner, allowing enterprises to manage and analyze real-time data generated by connected physical assets.
Wiliot’s technology is based on battery-free IoT Pixels, designed to generate continuous item-level data across supply chains and retail environments. Through integration with Databricks’ lakehouse architecture, organizations can combine physical-world data with enterprise data sources for analysis and operational use.
The combined system leverages Databricks’ computing, storage, and data management capabilities to process real-time data streams from Wiliot’s network. This setup enables organizations to apply analytics for supply chain monitoring, inventory management, and logistics optimization within a unified data environment.
The integration also supports the development of data-driven applications linked to physical assets, allowing organizations to adapt solutions based on specific operational requirements.
“Running the Wiliot Physical AI platform on Databricks gives our customers the ability to operationalize physical-world data at scale,” said Adi Applebaum, Wiliot’s Vice President of Product. “By aligning our platform with Databricks, Wiliot is providing customers with a robust, scalable foundation, plus advanced Wiliot solutions, for turning billions of data points into immediate, profit-driving decisions.”
Wiliot’s Physical AI platform supports five core supply chain solutions:
- Inventory Intelligence, which provides continuous visibility across storage areas, backrooms, yards, and vehicles
- Automated Receiving, enabling confirmation of inbound shipments without manual checks
- Automated Shipment Verification, ensuring outbound shipment accuracy in real time
- Reusable Asset Tracking, monitoring movement and usage of returnable assets
- Temperature Monitoring, tracking environmental conditions for goods during transit and storage
By using Databricks as its data infrastructure, Wiliot enables centralized data governance and access across different business functions, including operations, logistics, merchandising, and sustainability teams.
“Physical AI represents the next frontier of data intelligence," said Roberto Robles, Databricks GTM Lead Consumer Goods & Retail. "By partnering with Wiliot, we are enabling organizations to unify physical-world data and enterprise data to drive deeper insights and smarter decision-making. From the warehouse floor to the store shelf, this partnership empowers retailers to power truly intelligent, connected store experiences, where real-time product location, temperature, freshness, and inventory signals converge to eliminate out-of-stocks, reduce shrink, and personalize the shopper journey. Together, we are helping companies move their supply chains from reactive monitoring to proactive, Physical AI-driven automation at scale.”
The partnership is part of Wiliot’s broader effort to expand its technology ecosystem. Databricks has been recognized by Gartner® as a Leader in the Magic Quadrant™ for Cloud Database Management Systems for five consecutive years.
Wiliot continues to deploy its platform across sectors including retail, consumer packaged goods, and logistics. Companies such as Walmart and Royal Mail are currently using the platform to support operational processes.
“Organizations are looking to bridge the gap between their physical operations and their digital AI strategies,” concluded Applebaum. “This partnership accelerates Wiliot’s mission to bring intelligence to every product and asset by combining our real-time sensing with Databricks’ powerful data engineering.”