PCEA.NET Artikel: Siemens Achieves Breakthrough in PCBA Quality Using Causal AI from Xplain Data
We’re excited to share that our customer, Siemens Motion Control, has been featured in the latest edition of the PCEA…
We’re excited to share that our customer, Siemens Motion Control, has been featured in the latest edition of the PCEA…
Xplain Data has been named a sample Vendor for Causal AI in Gartner’s Hype Cycle for Artificial Intelligence, 2025, affirming…
Zorneding/Munich, 10. June 2025 – Xplain Data has joined Siemens Xcelerator – an open digital business platform that helps companies…
Award for Causal AI: Xplain Data has been honored with the F.A.Z. AI Innovation Award 2025 for its patented Causal…
Causal AI: Driving the Next Wave of Smart Manufacturing The 2025 State of Smart Manufacturing Report from Rockwell Automation reveals…
Xplain Data Featured in GARTNER® REPORT Emerging Tech Impact Radar on Artificial Intelligence, Dec 2024 We’re pleased to share that…
Causality cannot be proven from observational data. However, with comprehensive data, you can get close to proof. The more comprehensive your data, the lower the risk of misinterpreting correlation as causation. Comprehensive data involves a multi-layered data model.
Our ObjectAnalytics Database is designed to store complex data, such as millions of patients with billions of events (diagnoses, prescriptions, genomic data, etc.). Our Causal Discovery algorithms utilize this object-oriented data storage to efficiently search for direct and indirect explanations for a target event, uncovering potential cause-and-effect relationships.
Why not develop next generation intelligent algorithms by operating on entire objects instead of tables, rows, and columns?
Unleash your creativity to build analytical applications with whole objects at your fingertips!
Easily understand causation beyond correlation, based on a holistic view of your business objects.
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