NanoFlux Bio is building an AI monitoring system for bioprocess development — a self-learning digital twin that predicts, monitors and optimises, feeding real-time, self-cleaning sensing.
Inside the bioreactor, biology moves fast and information moves slow. Teams react hours too late, waste water on fouled sensors, and can't afford the analytics that would close the gap.
Off-line analytics come back 4–6 hours after sampling. By the time a problem shows up, the batch has already drifted.
Probes clog mid-run, forcing manual CIP/SIP cycles — 150–200 L of WFI water per week, plus downtime and recalibration.
Metabolite monitoring costs €80K+ per line — out of reach for the majority of mid-size CDMOs and biotechs.
The result is a black box: teams that see their process hours too late, and pay in water, reactor time and failed batches to learn what a model could have told them.
NanoFlux is built in layers. Each is useful on its own, and each is funded by proof from the one before — so the vision is reached without a leap of faith. We are live at layer one.
A self-learning twin across 9 cell lines that predicts, monitors and optimises a bioprocess from the team's own batch data — calibrated in days, validated to under 10% model error. The intelligence core of everything above it. Live today.
Our own real-time sensing module feeds the twin live — measuring metabolites inline and ending both the off-line delay and the fouling that wastes water and downtime. Affordable where advanced sensing has been locked away.
Every process the system sees sharpens the models. The AI stops predicting from literature alone and starts learning from the field — getting more accurate, plant by plant, run by run.
Sensing, twin and learning in one platform: the system sees the process as it happens, predicts where it's heading, and adjusts in real time. The biological black box, finally open.
Not a roadmap promise — a validated engine. It turns the batch data a team already has into a working, predictive model of their own process.
AI you can trust: every model parameter is sourced from peer-reviewed literature and stored with its references — auditable by design.
One self-learning twin doing three jobs at once — and getting smarter with every process it sees.
Forecast biomass, substrate and metabolite trajectories before a single litre is inoculated — turning guesswork into a model.
Real-time sensing feeds the twin live, so the process is seen as it happens — not four to six hours too late.
The AI recommends feeds, scale-up and corrections, and learns from every dataset to sharpen the next call.
Bioreactor know-how built into software — modelled, tested, and traceable to the literature.
Kinetic ODE models for four organism families capture real biology — growth, substrate uptake, inhibition, oxygen transfer and pH — not just curve-fitting.
Every kinetic value is drawn from peer-reviewed studies and stored with its references and confidence level. The model can always show its work.
An automated suite of 63 tests holds the models to physiological bounds and under-10% error against literature, on every change.
A calibration engine fits the models to real experimental data, so the twin moves from literature priors to a team's actual process — and improves as data grows.
A founder team pairing bioprocess domain knowledge with software and AI engineering, backed by Denmark's leading deep-tech institutions.
Leads strategy, commercialisation and the Australian build-out, connecting NanoFlux to the bioprocess and life-science ecosystem.
Leads the AI digital-twin platform, model engineering and the sensing-hardware roadmap out of DTU Skylab.
Building in bioprocess, or working with teams who are? We'd like to hear from you.