Agentic digital twins for accelerated process development
Build a digital twin from the runs you already have, test conditions virtually, and send only the best experiments to the lab.
Discover your optimal process conditions virtually with AI-enabled simulation.
A blend uniformity study on a 200 L pilot-scale mixing tank: 240 virtual runs across speed, fill level and feed point, and one condition worth testing in the lab.
Ask the questions you would normally answer with a run.
The agent answers from your model, shows the math, and tells you how confident it is.
Customer case studies from the unit operations you run.
Bioreactors
Optimize feed and process parameters for titer and quality in fed-batch and perfusion culture.
Mixing and blending
Find the speed, fill level and feed point that blend within spec at the lowest power.
Solvent swap distillation
Reach residual solvent spec with fewer put-and-take cycles, less solvent and shorter cycle time.
Start with what the data you have.
Hybrid models start from physics, so they need far fewer runs than a purely data-driven model. Most teams start with the bench and pilot data they already have.
Hybrid models start from physics, so they need far less data than purely data-driven models.
Build from a process model template, or bring your own.
Deploy hybrid digital twins that combine first-principles physics with machine learning for accurate process simulations.
Run virtual experiments with your digital twin
Publish your process model as an application your whole team can use. Test dozens of what-if scenarios, compare predicted outcomes and confidence ranges, and use the results to determine the optimal way to run your processes.
Set conditions in the app, or ask the agent a what-if question
Predicted critical quality attributes, confidence intervals and the parameters driving them
Send only the recommended runs to the lab
Lab results refit the model, and the next answer is sharper
Clone selection for perfusion, done in silico.
A hybrid digital twin built on historical Ambr15 fed-batch data predicted how each clone would perform in 3L perfusion. The team ranked candidates virtually and only took the best into lab-scale validation.
Process understanding that carries from development to commercial.
The model you build in development can help inform your QbD approach, and stays traceable once the process moves toward GMP.
Proven acceptable ranges for each CPP, with the model and runs behind them.
Sensitivity, edge-of-failure and parameter rankings as structured evidence.
Scored on held-out runs, with confidence intervals on every prediction.
Versioned models, change control and e-signatures.
Your first model in hours, not months.
You do not need a modeling team. Here is a typical first session, and who does what.
Questions process development teams ask
No. Bring designs in from JMP or MODDE, and send the recommended runs back out. Basetwo adds the model behind the design, so fewer of those runs have to happen in the lab.
The Basetwo agent builds it for you. Describe the unit operation and share any equipment specs or data, and the agent drafts the model, fits it to your runs and adds it to your flowsheet for review.
Our models are science- and physics-driven, so they are less sensitive to the range of the training data than empirical models are. Every prediction comes with a confidence interval that widens as you move away from your data. The agent flags when a question is outside the validated range and recommends the runs that would close the gap.
In your own workspace, with role-based access through your SSO. Connections to your systems are read-only, and every change to data or models is versioned and auditable. Your data stays yours: it is never shared with other customers or used to train global models.
Bring your last DoE. We'll show you what the next one should be.
