Culteon
Autonomous culture and feed control for mammalian and microbial lines.
Biologics Manufacturing
Bioseon
The autonomous operations layer for biologics manufacturing — autonomous cell-culture and fermentation control, AI titer and CQA sensing, purification control.
Overview
What Bioseon does.
Bioseon closes the loop on biologics manufacturing: autonomous cell-culture and fermentation control, AI titer and CQA sensing, harvest and purification control, contamination intelligence and the bioreactor digital twin.
Every run produces labelled process traces that train the next model, tightening the loop from seed to purified drug substance.
| Detail | Value |
|---|---|
| Stage | Seed |
| Category | Physical AI |
| Year | 2026 |
| Website | bioseon.com |
| Headquarters | Not disclosed |
| Raised | $2M |
| Investor | 10xC |
| Model | Per-bioreactor software subscription with a yield-outcome component. |
| Measured by | Titer and CQA conformance, yield per batch, contamination deviations, lines expanded per customer. |
Products
One loop, module by module.
Every deployment on bioseon.com writes labelled traces back into the model, so the system generalises across plants, alloys, grades and geometries.
Titeon
AI titer and CQA sensing from at-line and in-line signals.
Purion
Harvest and purification control across capture and polishing.
Sentira
Contamination and deviation intelligence.
Twineon
Bioreactor digital twin for scale-up and what-if.
Why we invested
The thesis behind Bioseon.
Biologics manufacturing runs on scarce process scientists and on off-line assays that report hours after the decision. Titer and CQA are read too late to act on.
Bioseon moves the read onto the line and the control into software. The buyer is a CDMO or biotech paying in hard currency; the wedge is one upstream step, and yield per batch is the number that opens the next module.
Who is building it
Co-Founder & CEO
Co-Founder & CEOOwns the CDMO relationships, the pilot programme and the commercial model.
Co-Founder & CTO
Co-Founder & CTOOwns the culture and purification models, the edge runtime and the audit architecture.
Building something similar?
Building in Physical AI? We want to hear about it.