Member, British Neuroscience Association
Founder & CEO, UndosaTech
"During my research into the role of Substance P in the axial elongation of myopia, I hit a wall. The data I needed was scattered across institutions, locked behind governance barriers, and months away from access. I was spending more time fighting for data than doing science."
UndosaTech was built to solve exactly that problem — starting with the vision science and neuroscience research community. We are building the infrastructure that has been missing — secure, federated, governed — one research community at a time.
A researcher may have access to data at their own institution — but the datasets they need sit at hospitals across the country, locked behind governance requests, or buried in lab servers as unpublished dark data that science has never seen.
Published papers represent a fraction of the data institutions generate. Negative results, inconclusive trials, and archived cohorts contain extraordinary scientific value — but no current mechanism exists for institutions to make this data available for governed secondary research, recover the cost of stewarding it, or share in the public benefit it produces.
UndosaTech's Research Data Reuse Programme is designed to change that — within the framework of institutional governance, patient consent, and ethics committee oversight. Institutions retain full control. Data never leaves its source. Reuse happens only under explicit governance agreements, with full audit trails and clear public-benefit criteria.
Summary statistics. Effect sizes. P-values. Conclusions drawn from data that the reader can never access. Science built on shadows of the underlying reality.
The raw OCT scans. The MRI sequences. The negative results. The inconclusive trials. The unpublished cohorts. Under proper governance, this data can support more representative, more reproducible science — informing models that current published datasets alone cannot.
A cloud-native SaaS platform unifying fragmented biomedical datasets with no-code AI tools — within HIPAA- and GDPR-aligned frameworks. We do not ask institutions to hand over their data.
AI models travel to local institutional servers to train. Datasets never leave the institution. This architecture is designed to support institutions in meeting GDPR, HIPAA, and EHDS obligations — reducing the data-transfer burden while keeping institutional governance, consent, and audit responsibilities intact.
Automated model building, predictive analytics, computer vision, and deep learning workflows — so neuroscientists and clinicians stay in charge of the science without needing a data science team.
Built around HIPAA, GDPR, and the EU Health Data Space Secure Processing Environment. Automated data lineage, audit trails, and support for Predetermined Change Control Plans where FDA submissions are in scope.
A governed framework that enables institutions to make archived and unpublished research data available for ethical secondary analysis — under their own governance, with patient consent honoured, and with public-benefit criteria built in. Institutions are supported in recovering stewardship costs through transparent, audit-friendly arrangements.
Rooted in Real Research: UndosaTech emerged directly from active neuroscience research — specifically a literature review into the role of Substance P in the axial elongation of myopia. The data fragmentation encountered during that process is the exact problem this platform is being built to solve. Research areas supported include Alzheimer's & dementia, Parkinson's disease, neurodegeneration, vision science & retinal imaging, treatment-resistant depression, and chronic pain.
The federated learning and biomedical data infrastructure space includes well-funded players such as Owkin, Lifebit, Rhino Federated Computing, and TriNetX, alongside national health data initiatives across the UK and EU. UndosaTech does not aim to compete with these platforms as a general-purpose biomedical infrastructure provider.
Instead, UndosaTech focuses on a specific, underserved wedge: vision science and neuroscience research, with a governance-first reuse framework, an institutional cost-recovery model designed for NHS Trusts and research universities, and a no-code interface built for domain researchers rather than data engineers.
Specialisation, not breadth, is the strategy.
Predictable recurring SaaS income combined with marketplace and partnership revenue that scales as the network grows.
Annual licences tiered by institution size: Starter (small labs), Professional (departments), Enterprise (pharma / NHS Trusts).
A percentage of each royalty transaction on the Neuro-Marketplace when anonymised datasets are licensed to third parties. Scales with network size.
Bespoke data access agreements with pharmaceutical companies for curated, federated neuroscience datasets. High-value, low-volume contracts.
Premium tier covering automated regulatory reporting, data lineage tracing, and PCCP management for FDA submissions.
Usage-based pricing for organisations integrating UndosaTech's AI models and data connectors into their existing research pipelines.
Enterprise ACV: £85K · Academic ACV: £18K
Gross Margin: 72% · CAC Payback: <14 mo
Net Rev Retention: 115%+
UndosaTech is founded by a medical doctor with a neuroscience research background. We are actively building our founding scientific and engineering team.
MBBS · MSc Applied Neuroscience (University of Dundee) · Member, British Neuroscience Association. Founded UndosaTech from direct experience of the data fragmentation problem in neuroscience research.
Seeking an experienced ML engineer with background in privacy-preserving machine learning, federated systems, or biomedical AI. Equity available. Remote-friendly.
Actively seeking a Scientific Co-Founder or Advisor from a leading NHS Trust or research university with expertise in neuroscience, biomedical data, or clinical AI. Equity available.
The timing for UndosaTech is not arbitrary — three structural shifts are creating a narrow window for the right infrastructure to dominate this space.
Cross-border secondary use of health data is now legally enabled for the first time across the EU. Institutions and platforms with compliant infrastructure in place first will capture the market. UndosaTech is designed natively for this moment.
GPT-4 and its successors have democratised model capability. The competitive advantage in biomedical AI now lies entirely in proprietary, high-quality, labelled datasets. UndosaTech sits on that asset — and builds the infrastructure others depend on to access it.
The royalty marketplace directly addresses a real and urgent need — turning archived patient cohorts into a revenue stream resonates immediately with budget-constrained Trusts and universities. The incentive to participate is financial, not just altruistic.
We are raising £900K pre-seed and building our founding team. If you understand the intersection of healthcare, AI, and regulatory complexity — we want to hear from you.
MBBS · MSc Applied Neuroscience
SAFE / Convertible Note · 18 months runway
Building for the NHS & global research market