Seeq + Databricks: Manufacturing Analytics Powered by AI
The combination of Seeq’s AI-driven manufacturing analytics with the Databricks Lakehouse Platform empowers process industries to unify, analyze, and act on their data at scale. Seeq connects directly to time-series, relational, and contextual data stored in Databricks, providing engineers, data scientists, and business leaders with intuitive tools to build insights faster. By leveraging Workbench, Organizer, Data Lab, and AI Agents, teams can perform advanced modeling, automate reporting, and collaborate seamlessly across IT and OT environments.
“Our collaboration with Seeq unlocks tremendous value for customers, making it simpler for organizations to operationalize and democratize loT datasets by leveraging the open and secure foundation of the Databricks Lakehouse Platform. This significantly lowers the barrier to data-driven innovation in the industry.”
Strong partnerships with Databricks create better results
This innovative partnership simplifies access to high-quality asset and process data, unifies IT and OT data processing, and accelerates AI/ML adoption across industrial manufacturing sectors. This integration enables scaling predictive analytics, asset performance monitoring, maintenance optimization, and process quality improvements across the enterprise. With the flexibility of Databricks manufacturing analytics and the domain expertise built into Seeq, organizations can move from siloed datasets to a unified platform for real-time monitoring, anomaly detection, and machine learning applications. Industries including oil and gas, chemicals, pharmaceuticals, energy, power, food and beverage, mining, and manufacturing benefit from faster insights, reduced downtime, and measurable ROI. Together, Seeq and Databricks accelerate digital transformation and make advanced analytics accessible across the entire enterprise.
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SEEQ + DATABRICKS
Explore the next generation of industrial intelligence with solutions built to optimize performance and innovation.
