A new University of Nottingham commercialisation venture, BioSee AI, has launched to address one of industrial biotechnology's most costly and persistent challenges - the lack of real-time visibility inside bioreactors to detect and avoid process failures and waste.
This new technology is designed to make advanced monitoring accessible to small and medium-sized biotechnology companies and can support a range of biological production processes, including: Alternative proteins, brewing, waste valorisation, bio-nutrition, biofuels and bioprocess developers.
Developed by Dr Oliver Fisher and Associate Professor Asma Ahmed from the University of Nottingham's Department of Chemical and Environmental Engineering, BioSee AI is developing a low-cost, non-invasive, AI-enabled multi-sensor platform that provides continuous insight into biological processes that are currently monitored using manual sampling and offline laboratory assays.
Across alternative proteins, algal biotechnology and waste-to-value bioprocessing, these traditional methods can delay detection of contamination, clumping or quality drift until batches worth hundreds of thousands – or even millions – of pounds are already at risk.
The BioSee AI platform integrates ultrasonic and optical sensing with multimodal machine learning models to predict key biological and structural parameters in real time. By providing early-warning detection, optimised harvest timing and improved yield consistency, the system aims to reduce batch failures, cut waste and unlock greater productivity across the UK bioeconomy.
The platform has secured support through the EPSRC Impact Acceleration Account (IAA) and funding from the National Alternative Protein Innovation Centre (NAPIC), supported by BBSRC, to accelerate development and commercialisation of the technology. These projects are being delivered in partnership with industrial collaborators BioPowers Technology and AlgaeCytes, to validate the system in fungal single-cell protein fermentation and algal bioprocesses.
A unique feature of the BioSee AI platform is its ability to learn and improve across sites without requiring companies to share proprietary process data. Modular, affordable and retrofit-compatible, BioSee AI is designed to bridge the gap between high-cost spectroscopic systems and low-information probes, making advanced monitoring accessible to small and medium-sized biotechnology companies.
Despite advances in automation, most industrial bioprocesses still rely on manual sampling and indirect probe data to interpret biological state. BioSee AI is about making the invisible visible by delivering affordable, real-time biological insight without invasive probes, and helping manufacturers prevent costly batch failures before they happen.
The BioSee AI team aims to progress from current proof-of-concept deployments to industrial pilot trials over the next 24–36 months, with the long-term ambition of supporting adaptive, AI-enabled control across fermentation and circular bioeconomy sectors.
As part of this next phase, BioSee team is undertaking structured market discovery through the national ICURe programme, engaging directly with bioprocess operators, equipment manufacturers, sensor companies and R&D teams to validate commercial demand, integration pathways and procurement drivers. The team is actively seeking feedback from across the wider biotechnology community to ensure the platform is shaped by real industry needs and scalable adoption routes.
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