DataHow offers an AI‑enabled platform, DataHowLab, that combines hybrid modeling, transfer learning, and digital twin technologies to turn raw bioprocess data into actionable intelligence. The solution provides predictive models and end‑to‑end analytics that accelerate process development, improve yields and robustness, and reduce time and cost for biopharma and biotech manufacturing teams.
Funding
Funding not disclosed
1OFounders
Product
Problem
Bioprocess development in biopharma relies on extensive experimental work and legacy data analysis methods, leading to long development cycles, high costs, and suboptimal yields and product quality. Companies often struggle to extract actionable insights from large, heterogeneous process datasets, limiting their ability to predict performance and accelerate scale‑up.
Solution
DataHow provides an AI‑enabled platform, DataHowLab, that combines hybrid modeling, transfer learning, and digital twin technologies to turn raw bioprocess data into actionable process intelligence. The platform integrates AI algorithms with encoded process knowledge to generate predictive models that guide experiment design, optimize yields, and improve robustness across development and manufacturing stages. By offering a structured framework and analytics services, DataHow accelerates digital transformation for biopharma teams, reducing development time and cost while enhancing product quality. The solution is process‑agnostic, supporting mammalian, microbial, cell‑ and gene‑therapy, and mRNA manufacturing workflows.
Target Audience
Primary customers are bioprocess development and manufacturing teams within large pharmaceutical companies and biotech firms seeking to accelerate process optimization and implement AI‑driven digital twins.
Features
- Hybrid models that fuse machine‑learning with mechanistic process equations for accurate predictions
- Transfer learning capabilities that leverage insights from previous projects to new bioprocesses
- Digital twin simulations enabling virtual experimentation and scenario analysis
- End‑to‑end data management and analytics pipeline that cleans, integrates, and visualizes process data
- DataHowLab interface that guides scientists through model building, validation, and decision support
- Customizable consulting services for digital readiness, strategy development, and model implementation