The service designs, tests, and executes AI driven Design of Experiments (DoE) and active learning frameworks to predict and optimize critical process parameters such as fermentation yield, titer, and cell growth rates. In our Biosolutions Lab, we deploy Deep Neural Networks (DNN) and Bayesian optimization for automated parameter tuning at micro scale, combined with human in the loop decision support for pilot scale biomanufacturing. The purpose is to help biotech enterprises and bio manufacturers identify process bottlenecks, increase batch yields by 15 to 35 percent, and cut experimental trial cycles by up to 50 percent.
Data from experimental setups that identify bottlenecks, support parameter optimization, and provide a documented basis for further process development, scaling, or implementation. This AI optimization approach reduces physical experiment rounds by up to 50 percent while delivering yield improvements between 15 and 35 percent, building on proven industrial fermentation and microbial protein scale up projects.
The service is delivered as an experimental process development activity including design of experimental setups, analysis of critical process parameters, testing of DNN and Bayesian optimization, and evaluation of human-in-the-loop decision support, followed by documentation of results and recommendations.
Biosolutions