The Pitch That Landed $1.2M in California
While studying biomedical engineering in London, Sebastian Kot grew frustrated with a stubborn problem: healthcare AI models perform poorly not because they are badly designed, but because hospitals and insurers are terrified to share the data they need. The fear of sensitive records leaking into the wrong hands keeps most real-world medical data locked away, starving algorithms of the raw material that could make them clinically useful.
Kot set out to solve that by building a system that lets AI work with health records without ever actually seeing them. The concept attracted $1.2 million in seed funding from Long Journey Ventures, a California-based fund. The deal came together not through a formal pitch round but after a single dinner in the US, a striking contrast to the “no” he says he heard repeatedly from European funding sources, which turned him down because of his age and the project’s early stage.
The young Slovak, who previously won a global quantum computing hackathon run by Quantinuum and explored deterministic reasoning in AI at the London Initiative for Safe AI, named his company London Quantum Group. Its core infrastructure is designed to preserve privacy so tightly that even the model operator cannot access the underlying patient records, a capability that would unlock vast hospital data troves currently too sensitive to touch.
Why European Funds Kept Saying No
The Age Gap in European Funding
Kot’s experience is not an isolated one. European early-stage capital remains unusually risk-averse toward founders who lack a track record or who are barely out of their teens. While US investors frequently write small cheques based on a team’s technical talent and the size of the problem they are tackling, European grant bodies and many local VCs demand proof points that young deep-tech teams simply cannot yet supply. This difference helps explain why Kot found his first real backing 9,000 kilometres from the universities and labs where he built his reputation.
Privacy-Preserving AI Becomes a Practical Necessity
The bet on invisible health data lands at a moment when regulators are tightening rules around medical AI and hospitals face growing pressure to improve outcomes without compromising confidentiality. London Quantum Group’s approach—allowing AI to be trained on records that remain fully encrypted or processed in ways that reveal only statistical patterns—fits into a broader push toward privacy-enhancing computation. If the technology lives up to its promise, it could finally give healthcare AI access to the massive, diverse datasets that today sit behind compliance firewalls, transforming the quality of diagnostics, treatment planning and drug discovery. But the path from research vision to a product that hospital IT departments trust is long, and $1.2 million is only enough to prove the first mile.
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