Characterize, scale up, and validate processes with confidence
Optimize upstream and downstream process development across scales with Basetwo’s low-code AI platform.
Build reliable process
models from high throughput
Go beyond traditional DoE and response surface modelling. With Basetwo's platform, scientists can build highly scalable hybrid ML-process models and reliably optimize control spaces from HTPD data (i.e. Ambr-250, RoboColumn, PreDictor well-plates, etc.).
Rapidly screen and optimize consumable productivity and utilization.
Build machine learning and process models to predict the utilization and degradation of consumables such as resins and filter media.
Build scale-up process models with confidence.
Enable cross-learning from previous scale-ups by training machine-learning models with historical manufacturing data, even from related processes at different scales.
Easily ingest data from multiple systems.
Integrate with leading laboratory information management systems (LIMS) to ingest critical data from lab-scale experimentation. Moreover, Basetwo is able to easily integrate with data that is already hosted in a cloud-based data lake (i.e. ADLS).