Our Beliefs.

We believe that AI can empower all industries. Therefore, we accelerate the adoption of AI techniques by providing a unique privacy-preserving framework for federated learning and knowledge-sharing among businesses.

We believe in a symbiosis between humans and machines. We use algorithms to empower humans, and, in return, humans improve the algorithms. We build trust by exposing and explaining the inner-workings of these algorithms. We help create actionable insights and automations out of data, check the data for discrimination and bias, and explain decisions created out of the data.


From Data to Insights

We love both machine learning and machine reasoning. Also formal methods, optimisation, genetic algorithms and game theory. We used these on anomaly detection, threshold analysis, risk analysis, preventive maintenance, and numerous automations. They do work.

System Architecture

Have you ever seen home renovation TV series? When something beautiful gets created in front of your eyes in real time. Did you get inspired? What they do not show us is all the tedious pre-work they’ve been doing before the show. Same applies to AI: one needs tool integration, data lake/stream for aggregation and augmentation, knowledge base and other aspects of system architecture to be in place before you are ready to push the button and see the full benefit of AI.

Linking the Insights

Many years ago there were state machines and ontologies. We used state machines to model procedural knowledge, and ontologies to model declarative knowledge. Then we figured that linking both is a pretty smart way of figuring out and executing automations. No hands.

Privacy Framework

Let us share the data with each other, for a good cause. Maybe even open APIs for developers and let everyone innovate of top of enterprise systems and data. Maybe not. Not unless you are sure no one is tapping into your unique know-how without getting anything in return. Or misusing your systems. We’ve been working with trustworthiness for mission-critical systems for years, and have seen it all. Don’t do it unless there is a privacy framework that you can trust.

Let’s make something together.

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