1,500,000+ data points · every second of measurement
Your bioprocess AI is only as good as the quality and quantity of data it gets.
You already run a data-driven process.
Probes, soft sensors, multivariate models — all working to understand what your cells are doing. But nearly all of it runs off one bulk number: an average across millions of cells. Your models are only as good as that input — and an average hides most of what’s happening.
An average can’t tell you what your cells are doing.
- One number for millions of cells. Bulk measurement collapses the whole population into a single average.
- The average hides what matters. Viability, subpopulations, cell health and stress all disappear into the mean.
- You find out too late. By the time a shift finally moves the average, it’s often too late to act.
- Your models inherit the blindness. Fed an average, a model can only ever see an average.
And that blind spot has a price.
- Problems surface late — after the average finally moves, sometimes too late to save the batch.
- Unpredictable batches — batch-to-batch variability you couldn’t see coming.
- Slow, empirical development — the signal that would explain it stays hidden in the mean, so you run experiment after experiment.
- Your AI stalls — a model fed averages can’t learn what it never saw.
The signal your AI has been missing.
Amphasys impedance flow cytometry measures every cell individually, label-free, at multiple frequencies. Take the raw stream and model your own parameters with AI — or use the up-to-40 parameters per cell we already analyse:
Across bacteria, yeast and mammalian cells — quality (every cell) and quantity (millions of data points), the two things your AI is short of.
How it runs: a fast, at-line measurement — draw and dilute a sample, get results in minutes, and repeat as often as your process needs.
Give your AI every cell — here’s what it can finally see.
See — every cell, individually
Not one number for millions.
Understand — how the population shifts
Heterogeneity, subpopulations, cell health — everything the average blends away.
Catch early — before it’s too late
See a shift while it’s still in a few cells — before the average moves.
Feed your AI — data a bulk number can’t give
Up to 40 per-cell parameters as model input — a level of data quality an average can’t match.
Better data in, better models out — because your AI finally learns from the cells, not the average.
Proven, open, and rich by design.
Proven, not promised
An established, commercially deployed single-cell measurement platform — a mature technology, not a concept or a pilot.
Open, not a black box
The data layer that plugs into the models and PAT stack you already run. You keep your stack; we make it see every cell.
Rich by design
Up to 40 analysed parameters per cell, from over 1.5 million data points per second — quality and quantity in one measurement.
Take it analysed, or model your own.
Are 40 analysed parameters per cell enough? Here you go — have at it. Need more, or want to model your own from the 1.5 million data points per second? Let’s customize. Either way, it starts with a conversation.
Questions, answered.
How is this different from OD600 or offline cell counting?
OD600 and bulk probes give one number — an average across millions of cells. We measure each cell individually: thousands per sample, up to 40 parameters each. You see what the average hides.
What exactly do you measure per cell?
Up to 40 parameters per cell — viability and membrane integrity, count and concentration, size and volume, metabolic state (cytoplasmic conductivity), and population heterogeneity — from one label-free measurement.
Why does single-cell resolution matter for AI?
A model fed an average can only ever see an average. Per-cell data lets it learn from the heterogeneity, subpopulations and cell health that a bulk number blends away.
How much data is that?
During a measurement the device acquires over 1.5 million data points per second. Per measurement (~10,000 cells) we deliver up to 400,000 analysed parameters — or hand you the raw stream to modulate your own with AI.
How does it fit my existing models and PAT stack?
It’s an open data layer, not a closed platform — the structured per-cell output is made to feed the soft sensors, digital twins and models you already run.
Let’s find the setup that fits your process.
Tell us what you’re trying to run and model — we’ll tailor the data to your models. The technology’s proven; the fit is what we work out together.
