Physics-informed AI that predicts your COGS/kg, titer, rate, and yield from as few as 5 lab runs. No more failed scale-ups.
The jump from lab to production changes everything—fluid dynamics, heat transfer, mass transport, cellular stress. Empirical data alone can't predict what happens next.
Traditional approaches require hundreds of runs and months of pilot work. Most companies discover their true COGS after they've already committed the capital.
One platform that connects your lab data to production-scale economics, with physics-informed predictions at every step.
Neural networks constrained by mass balance, transport equations, and Monod kinetics. Not black-box ML—five lab runs is enough.
Every simulation outputs COGS/kg with confidence intervals and sensitivity analysis. Know your cost levers across USP and DSP.
Hydrodynamic modeling for kLa, mixing time, tip speed, and dissolved oxygen at production scale. Know risks before committing capital.
Nine unit operations — including foam fractionation for biosurfactants — with organism-specific templates. Captures the 60–80% of production cost that most tools ignore.
Built-in profiles for seven common organisms plus a Custom flow for novel or engineered strains. Bring your own kinetics — or let the model learn them from your lab data.
Supported Organisms
Whether you're developing a monoclonal antibody, optimizing microbial fermentation, or evaluating a novel expression system, Augur fits your workflow.
Challenge: Spending months on pilot-scale campaigns to discover true production economics
With Augur: Predict COGS/kg from bench-scale data before committing to expensive pilot runs
Challenge: Manually calculating scale-up parameters with spreadsheets and rules of thumb
With Augur: Physics-informed modeling of kLa, mixing, DO, and cellular stress at any scale
Challenge: Building COGS models disconnected from actual fermentation performance
With Augur: Integrated USP-DSP economics tied directly to predicted titer, rate, and yield
Challenge: Making capital allocation decisions based on incomplete process understanding
With Augur: Scenario comparison with confidence intervals and sensitivity analysis
Drop CSV files from your fermentation runs. Augur auto-detects columns, handles encoding issues, and scores data quality before you proceed.
Set your production parameters—bioreactor volume, organism, DSP train. Start from organism-specific defaults or customize everything.
In under 30 seconds: production-scale COGS/kg, titer, rate, yield—with confidence intervals, risk factors, and sensitivity analysis.
Traditional ML needs hundreds of runs. Augur's physics-informed architecture embeds conservation laws directly into the model, enabling accurate predictions from minimal experimental data.
Conservation laws and transport equations as neural network constraints. The model cannot violate mass balance or energy balance.
Monod kinetics with product inhibition, overflow metabolism, and Arrhenius temperature dependence. First principles, not curve fitting.
USP through DSP cost modeling with Monte Carlo uncertainty quantification. Every prediction includes production economics.
Augur's physics-informed neural networks embed conservation laws, Monod kinetics, and transport equations directly into the model architecture. The result: predictions that obey the laws of physics, from 10× less data than pure ML approaches.
De-risk your scale-up decisions. Know your production economics before you build.