Seven products, ready today
FermentWin
V23 biology-native digital twin — predict, optimise, win every batch
An ML-powered digital twin for real-time fermentation monitoring and fault detection. The flagship has evolved through cognitive (V21), self-learning (V22) and biology-native (V23) generations, combining a genome-scale kinetic backbone with machine learning.
- Validated on 100 industrial penicillin batches with 100% fault-detection sensitivity
- Detects developing faults 40–80 hours earlier than conventional monitoring
- Biology-native engine: kinetic model + metabolic flux + genome-scale model
- Runs on-site — built by biology professionals, no IT team required
Demo video — coming soon
FerMediaOpt
Strain-aware fermentation media & process optimiser
A bioprocess R&D companion that finds the ideal nutrient composition for your organism. Three modes — a calibration calculator, a dry-lab predictor, and a stoichiometric feasibility checker — backed by four mechanistic scientific layers grounded in peer-reviewed literature.
- Three modes: Calculator, Dry-Lab Predictor, and Stoichiometry Check
- Plackett-Burman & RSM statistical engine, validated to R² = 0.983
- Strain-specific calibration with bootstrap confidence intervals
- Built-in strain library with elemental mass-balance bottleneck detection
Demo video — coming soon
CellWin
CHO cell-culture digital twin — mammalian bioprocessing
A biology-native digital twin for industrial CHO cell culture, built on the latest community-consensus genome-scale metabolic model. CellWin brings the FermentWin approach to mammalian cell culture — a key differentiator from microbial-only platforms.
- Built on iCHO3K — 8,368 reactions, 4,695 metabolites, 2,929 genes
- Growth rate predicted to 1.7% error against measured fed-batch data
- Dynamic flux-balance simulation validated across multiple batches
- Integrates real CHO RNA-seq data with a transcriptomic foundation model
Demo video — coming soon
CellMediaOpt
CHO cell-culture media optimiser
The cell-culture counterpart to FerMediaOpt. CellMediaOpt predicts CHO growth, titer and product quality from media composition, with dedicated glycosylation and stoichiometry modules — extensively validated against the published literature.
- Forward predictor across multiple CHO strain profiles
- Glycosylation and stoichiometry analysis modules
- Validated against 6+ published studies (Capella-Roca, Combe, Walsh, and more)
- Designed to reduce wet-lab media-optimisation cycles
Demo video — coming soon
Ferment BatchAnalysis
Offline batch analytics with regulatory-style reporting
Drop in a batch CSV and get a deterministic engineering verdict plus three professional PDF reports. Optionally compare against a golden reference batch. Fully offline, with a hash-chained audit log for regulatory traceability.
- Automatic channel mapping and phase detection
- Basic, advanced and technical PDF reports generated per batch
- Golden-reference comparison for batch-to-batch consistency
- Audit-logged data container — runs entirely on your machine
Run it in your browser — pay-per-use with a free trial. Pricing coming soon.
Try online — request access
Demo video — coming soon
Ferment DOE
Fermentation-aware Design of Experiments
A desktop application that helps scientists design DOE studies, fit statistical models, and read plain-language AI interpretations of the results. Each study is a single self-contained file; the application runs entirely offline.
- Eight design types: Full/Fractional Factorial, Plackett-Burman, Taguchi, Box-Behnken, CCD, Simplex
- Full statistical engine: OLS regression, ANOVA, lack-of-fit, diagnostics
- Mixture designs with Scheffé canonical polynomials
- AI-generated, plain-language interpretation of every result
Run it in your browser — pay-per-use with a free trial. Pricing coming soon.
Try online — request access
Demo video — coming soon
ProteinWin
AI protein-gel reviewer
Automated review of protein gels powered by a trained deep-learning segmentation model. ProteinWin detects lanes and bands, estimates molecular weights, and produces a structured review — backed by a documented SOP library.
- Trained U-Net model for gel lane and band segmentation
- Automated molecular-weight estimation
- Structured review output with a built-in method knowledge base
- Documented SOP library for laboratory use
Upload a gel and review it in your browser — pay-per-use with a free trial. Pricing coming soon.
Try online — request access