Company Name:

Incubation Round:


Application Track:

Track 1

Proposed by:


Data Provider:


Challenge Name:

Data-Based Optimisation of Manufacturing Processes

Product Description Process Control makes it incredibly easy to model complex processes and create Self-Perfecting Digital Twins for hardware equipment, chemical processes and even entire production lines, enabling effortless cost & material optimization and unlocking up to 50% reduction in product time-to-market.

With Automated Machine Learning, Federated Learning and Hybrid Modelling, the Platform enables users to rapidly model and automate complex physical systems across multiple machines and process steps. AutoML allows for autonomous algorithm selection and hyperparameter search, enabling fast and accurate AI models. Federated Learning allows for privacy-preserving data analysis across machines from multiple vendors and process steps. The Simulation Engine generates physics-based model for complex processes, empowering incorporation of cyber assets along with physical ones.

Reach Timeline

*Expose phase is open to all Experiment phase teams




Manufacturing / Industry


Company maturity:

Pre-MVP and MVP

Investment level:


Funding raised:

< EUR 100,000

Collaboration opportunity:

Company Description is a Berlin-based AI startup specialized in digital transformation for precision manufacturers. The startup uses a unique blend of machine learning and physics-based simulation to create Self-Perfecting Digital Twins of complex processes, in order to minimize cost and time-to-market. The startup has validated their approach with the largest semiconductor factory in Europe, GlobalFoundries Dresden.

Calvin Ng

Founder and CEO, responsible for overseeing the project

Vadim Pinskiy

Cofounder and CTO, responsible for technical roadmap and strategy

Patrick Parkinson

Science Expert, responsible for Hybrid AI and physics simulation

Mark Ayzenshtadt

AI Expert, responsible for technical development and deployment