Harness the power of sample-efficient AI to optimize your process without the need for heavy investment in infrastructure, data scientists or large amounts of historical data. Designed to be leveraged by your SME’s and operators, Quartic’s optimization application allows you to fine tune your process for KPIs that have the biggest impact.Learn more
The first and only digital implementation of Reliability Centered Maintenance (dRCM) built for practitioners allows you to build early detectability agents for random failure modes using machine learning and rules. Agents provide real-time and predictive functional degradation risk, root causes and P-F trajectory that triggers alerts and actions to eliminate unplanned downtime.Learn more
The most advanced and complete implementation of MVDA modelling with machine learning to accelerate off-line investigations, real-time batch evolution and predictive batch performance monitoring. Product and batch data context and a common workflow for offline, on-line, and real-time monitoring reduces batch monitoring effort and scales robustly.Learn more
Finally implement true CPV by detecting deviation signals and trends with machine learning that were previously undetectable to deploy CPV. Use design space and control space understanding of CPP’s, CMA’s and CQA’s in real-time for evidence-based deviation investigations.
Automate off-line assays of spectral data for critical process and quality parameters to reduce analysis time, create near real-time, real-time, or predictive soft sensors for multiple parameters; use PAT for closed loop control.Learn more
Use small-data, sample efficient, explainable AI to conduct more efficient product characterization, design, and improvement experiments to reduce material consumption and time for PD.Learn more