Downstream Process Optimisation
Accelerate your path to viability by automatically testing thousands of processes in-silico, focus experimentation on the highest potential ones and continuously improve as data is uploaded.
CHEAPER & FASTER R&D
By focusing experiments
on the best processes
BOOST PRODUCTION
By optimising for higher product recovery & quality
REDUCE UNIT COSTS
Optimise process for end-to-end process economics
REINFORCE ADVANTAGE
Leverage historical data to drive new R&D projects
Quicker and cheaper R&D with AI Simulation
With over one trillion possible parameter combinations per processes, optimising bioprocesses is complex and involves navigating a web of interconnected variables.
The traditional way: Slow, expensive, manual & inefficient
Bioprocess optimisation is generally done through cycles of planning, conducting experiments , analysing, and interpreting controlled tests on a limited number of factors each time.
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This is incredibly expensive as the average cost for conducting an experiment typically ranges between $10k-100k, and scaling from lab to market takes between 3-10 years. Understanding the commercial and sustainability tradeoffs of each option is also slow and expensive - the cost of just one Techno Economic Analysis (TEA) report ranges between $20k - 40k and takes months to complete.
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Our way: Instantly test 1000s of processes virtually
New Wave Biotech accelerates biomanufacturing with AI-powered Bioprocess Simulation Software, providing tools to companies and scientists to virtually explore experiments, compare scenarios, optimise processes and leverage their data to build competitive advantage.
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Our platform's AI-powered bioprocess simulation predicts quantified output, costs and sustainability impact and improves as it learns from empirical-data, enabling you to foresee the impact of decisions.
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Meanwhile our in-house optimisation engine automatically simulates and compares tens of thousands of scenarios, enabling you to navigating the vast landscape of possibilities and identify the process options with the highest potential. This allows you to focus your valuable experimentation capacity where it will yield the most returns.​
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As experimental data is used to train your models for improved predictions, it enables you to leverage historical data to drive every new R&D project in your organisation.
