ScienceMachine, a fast-growing London-based AI startup accelerating biotech data analysis with a fully autonomous agent, has raised a $3.5 million pre-seed funding round.
The company’s autonomous AI agent, named Sam, functions as a 24/7 AI bioinformatician, automating the entire data analysis pipeline for biotech and pharma companies and enabling faster scientific discovery.
By automating internal workflows using AI, ScienceMachine introduced a fully autonomous AI agent that is already used by biotech clients. The company successfully delivered what many larger companies have yet to accomplish: real, production-grade AI automation that cuts costs and speeds up scientific discovery.
ScienceMachine solves a problem that severely slows down biomedical progress today: research teams in the life sciences face an overwhelming flood of complex biological data from labs and clinics but struggle to hire enough data scientists, a key role for them. In parallel, domain experts often lack the time or training to run sophisticated biotech analyses. This gap leads to delayed or missed scientific breakthroughs.
AI agent Sam closes this gap. It integrates directly with existing databases and lab workflows, and then continuously processes experimental data to find patterns, insights, and potential breakthroughs, without any manual intervention.
In effect, Sam provides researchers with the same output that would normally require an entire team of data scientists, significantly accelerating their work.
Lorenzo Sani, CEO and Co-founder of Science Machine, shared:
Our goal is to help biopharma make groundbreaking discoveries faster and cheaper. Our AI agent works around the clock, analysing research data from lab to clinic, turning raw data into breakthroughs in hours, instead of months. And we are only at the beginning. We feel like AI will truly transform research and discovery in the coming years.
Sam handles everything, from data cleaning to exploratory analysis to visualization, continuously and autonomously unlocking faster research cycles and dramatically reducing the cost of discovery.
Early customers report results a third of the time, at a fraction of the price, and of a higher quality than they could have achieved on their own.
One month after launch, ScienceMachine already has multiple contracts and a fast-growing pipeline driven entirely by inbound interest.
The round was led by Revent and Nucleus Capital, with participation from strategic angels.
Rebecca Brill, Principal, Revent, said:
ScienceMachine is one of the most impressive examples we’ve seen of pure execution. With just two people, they’ve built a product that’s not only best-in-class technically, but already delivering measurable value to customers. They’re perfectly positioned to disrupt one of the largest and most important markets in the world.
Maximilian Schwarz, General Partner at Nucleus, expressed confidence that agent-based architectures are poised to become the leading interface for scientific software:
ScienceMachine is ideally positioned to dramatically expand access to complex bioinformatics for wet-lab scientists and speed up iteration cycles, ultimately increasing the addressable market and accelerating R&D timelines.
The funding round will support product development and hiring, particularly in sales and pharma partnerships.
ScienceMachine plans to expand its reach from biotech startups into larger companies - where ACVs are higher and the need for scalable, flexible data automation is even greater.
Lead image: ScienceMachine founders | Photo: Uncredited
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