Xplain Data Causal Discovery algorithms enable companies in all industries to identify the few, potentially causal relationships in their “real world data” that are hidden behind a plethora of trivial correlations. Users can leverage this cause-and-effect knowledge to intervene in their business processes to eliminate the causes of errors or achieve a desired effect. Xplain Data customers include leading enterprises in the mechanical engineering, manufacturing, and healthcare sectors, which use the technology not only for sophisticated data analyses but also for predictive maintenance, yield optimization, and Care Management.
Xplain Data is 100% owned by our founding team.
“Each new idea passes through three stages. First, people will ridicule it. Second, it is violently opposed. Finally, it will be considered self-evident.”
– Schopenhauer (1788 – 1860)
Where we come from
In many analytics projects, the complexity of real-world data often clashes with the restrictive data structures that today’s Machine Learning algorithms can digest. Instead of forcing data into oversimplified structures, we felt that a concept was needed that can handle complex real-world objects “as they are”.
In 2015, we, therefore, set out to develop some groundbreaking innovations in the context of Big Data and Artificial Intelligence. The ObjectAnalytics paradigm emerged for that – a novel concept for working analytically with entire objects – and based on that our unique approach to Causal Discovery.
Innovation requires entrepreneurship – and an entrepreneurial cooperation model with early adopter customers. We are looking for further visionary customers and partners who want to bring leading-edge intelligence into their portfolios. We offer novel ways of cooperation such as our co-innovation model, which – instead of paying for services – shares risk and reward.
The buzz about Artificial Intelligence is ubiquitous, but there is no talk about causality. Can a system be intelligent without a notion of cause and effect?
Our mission is to bring viable concepts for causality into the domain of Artificial Intelligence. It requires a holistic view to the object of analysis, which we enable with a novel database technology.
For classic machine learning methods, data has to be cast into constrained analytical schemas, typically a flat table… while real-world data is much more complex than that.
Our vision is to enable Causal AI algorithms that process complex “objects” exactly as they are instead of in an artificially prepared analytics environment.
Want to head off with us?
Are you looking for a fresh view on analytics? Are you open to combine the strength of your established company with that of our agile, innovative startup? If so, we are looking forward to hearing from you!