Backed by founders from
Supported by
Supported by
Our products
Process
Optimization
Optimizes workflows to increase yield/quality/equipment availability and reduce energy/material waste.
Predictive
Maintenance
Predicts equipment failures to increase
equipment availability and reduce
energy/material waste.
Root Cause
Analysis
Finds root causes of issues to increase yield/quality/equipment availability and reduce energy/material waste.
Demand
Forecasting
Predicts future customer demand to optimize inventory/production schedules and reduce stockouts/excess inventory.
Inventory
Optimization
Balances stock levels with customer demand, maximizing profit while ensuring availability.
Smart Order
Recommendations
Optimizes order quantities, prevents over/under-ordering, reduces material waste and ensures availability.
Smart Energy
Management
Dynamically monitors and controls real-time power consumption to reduce utility costs and reduce energy waste.
Real-Time Material
Purchasing Insights
Provides real-time insights on the cost of
essential materials, empowering teams to
make cost-effective sourcing decisions.
Our team
Arda Arslan
Co-Founder & CEO
MSc Computer Science at ETH Zürich
• 4 years of work experience as an ML Engineer, including a Founding/Lead Engineer role at an ETH Zürich spin-off providing AI solutions to manufacturers. • Comprehensive experience in Operations Research, Statistical Machine Learning, Deep Learning, Software Engineering, and building production-grade, real-time ML systems. • Bachelor's degree in Industrial Engineering.
Dr. Sergey Litvinov
Co-Founder & CTO
Senior Scientist at Harvard & ETH Zürich, PhD at TUM
• 21 years of research experience developing algorithms for complex systems. 60+ peer-reviewed publications, and 1300+ citations. • Comprehensive experience in Materials Science, Computational Science, Statistical Machine Learning, Deep Learning, Software Engineering, C++, CUDA, and parallel computing. • Led software development teams.
Our team
Arda Arslan
Co-Founder & CEO
MSc Computer Science at ETH Zürich
• 4 years of work experience as an ML Engineer, including a Founding/Lead Engineer role at an ETH Zürich spin-off providing AI solutions to manufacturers. • Comprehensive Experience in Operations Research, Statistical Machine Learning, Deep Learning, Software Engineering, and building production-grade, real-time ML systems. • Bachelor's degree in Industrial Engineering.
Dr. Sergey Litvinov
Co-Founder & CTO
Senior Scientist at Harvard & ETH Zürich, PhD at TUM
• 21 years of research experience developing algorithms for complex systems. 60+ peer-reviewed publications, and 1300+ citations. • Comprehensive Experience in Materials Science, Computational Science, Statistical Machine Learning, Deep Learning, Software Engineering, C++, CUDA, and parallel computing. • Led software development teams.
Our products
Process
Optimization
Optimizes workflows to increase yield/quality/equipment availability and reduce energy/material waste.
Predictive
Maintenance
Predicts equipment failures to increase
equipment availability and reduce
energy/material waste.
Root Cause
Analysis
Finds root causes of issues to increase yield/quality/equipment availability and reduce energy/material waste.
Demand
Forecasting
Predicts future customer demand to optimize inventory/production schedules and reduce stockouts/excess inventory.
Inventory
Optimization
Balances stock levels with customer demand, maximizing profit while ensuring availability.
Smart Order
Recommendations
Optimizes order quantities, prevents over/under-ordering, reduces material waste and ensures availability.
Smart Energy
Management
Dynamically monitors and controls real-time power consumption to reduce utility costs and reduce energy waste.
Real-Time Material
Purchasing Insights
Provides real-time insights on the cost of essential materials, empowering teams to make cost-effective sourcing decisions.
Backed by founders from
Backed by founders from