Resources
Guides, case studies, and technical notes from The Bioforge team on AI-powered bioprocess optimization.
Classical design-of-experiments methods scale poorly beyond 5–6 factors. This guide explains how Bayesian optimization handles high-dimensional design spaces with far fewer experimental runs.
Read moreA detailed look at how Precision Biologics used BioOptima AI to compress their CHO upstream development timeline, reduce media costs by 34%, and reach target titer in three experimental cycles.
Read moreGaussian processes offer calibrated uncertainty quantification that black-box neural networks cannot match. This note explains the fundamentals and why they matter for small bioprocess datasets.
Read moreNot all historical batch data is created equal. This guide covers what makes a dataset model-ready — and how to clean, harmonize, and enrich run records before feeding them to BioOptima.
Read moreHighlights from our panel discussion with process development leaders at three leading cell and gene therapy companies on where AI adds real value in viral vector and T-cell expansion workflows.
Read moreAn analysis of recommendation accuracy across 23 scale-up events, comparing BioOptima predictions at 2L bench scale against outcomes at 50L and 200L production bioreactors.
Read moreWe publish one in-depth piece per month. No marketing emails.
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