Proximal Ventures has raised $27.5 million for its debut fund, targeting early-stage companies that use artificial intelligence to improve the services and infrastructure surrounding drug development. The fund was launched by Sahir Raoof, a former SignalFire investor who previously held business development and partnership roles at healthcare companies. Proximal plans to invest in pre-seed and seed-stage startups, with initial cheques ranging from $500,000 to $1.5 million. The strategy focuses on an industry where pharmaceutical companies spend heavily on specialised work, from synthesising compounds and scaling manufacturing to recruiting clinical-trial participants and analysing medical data. Raoof believes AI could change the economics of those services.
The fund targets the services behind pharmaceutical research
Drug development involves a long chain of technical and operational tasks. Before a medicine reaches patients, researchers must identify promising biological targets, design and test candidate compounds, establish reliable manufacturing processes and run clinical trials. Pharmaceutical companies often rely on external providers for parts of this work because building every capability internally would be expensive and slow. Those providers sell expertise, laboratory capacity, data services and operational support. Proximal’s investment thesis is that AI systems could automate or accelerate portions of these workflows, allowing new companies to provide services at lower cost or in less time. The opportunity is not limited to generating molecules; it includes the infrastructure that helps research move through the development process.
Raoof told TechCrunch that pharma has historically been viewed as a stronger buyer of services than software. That distinction matters because selling a software licence into a pharmaceutical organisation can require a different business model from provideing a complete service with measurable outcomes. An AI startup may need to combine models with domain experts, validated data, laboratory partners and regulated workflows. Customers are unlikely to accept a tool merely because it produces an interesting prediction. They need evidence that it improves an existing process without compromising reproducibility, documentation or quality controls. Proximal is So looking at companies that can translate technical capabilities into operational products that pharmaceutical buyers can adopt.
The firm has already backed seven startups, including ChronicleBio, which was co-founded by former OpenAI executive Fidji Simo. ChronicleBio uses AI to analyse biological and medical data to improving understanding of chronic diseases and identifying potential treatments. Proximal’s initial portfolio gives the fund exposure to different parts of the healthcare data and research landscape, although the available announcement does not provide detailed performance results for those investments. The firm intends to write early-stage cheques that help founders build products, recruit technical teams and establish initial customer relationships. Its capital base is relatively focused, making the ability to choose a specific problem and support commercial execution important.
Raoof is betting on operational AI, not only molecule generation
Raoof’s background combines venture investing with operating experience in healthcare technology. He spent two years as a principal at SignalFire, led business development at clinical-trial recruitment startup Iterative Health and previously oversaw partnerships at Flatiron Health, the oncology data company acquired by Roche for $1.9 billion in 2018. These roles gave him exposure to the commercial and data challenges that shape healthcare technology adoption. Proximal’s backers include SignalFire, Kleiner Perkins, Bertelsmann Healthcare Investments and individual investors with experience in healthcare and venture capital. Amy Abernethy, a Highlander Health co-founder and former FDA principal deputy commissioner, is also an investor, adding regulatory and clinical experience to the fund’s network.
The investment thesis still faces difficult execution questions. Pharmaceutical development is regulated, data can be sensitive and the cost of proving a new method can be substantial. AI-generated recommendations must be tested against laboratory evidence and clinical outcomes, while software used in regulated processes may need extensive validation and audit trails. Startups also face long enterprise sales cycles and must show that a product fits existing research systems. Proximal’s fund gives founders access to capital and industry relationships, but it does not remove these barriers. The fund’s progress will be measured by whether portfolio companies win repeat customers, show measurable improvements and build products that can survive scientific and regulatory scrutiny.