Today, we announced our $13.6M fundraise including a $12M series A led by Dimension Capital, with participation from Y Combinator.
With AI becoming a core part of life sciences organizations’ research, no longer part of experimental projects, but embedded in real drug discovery problems, scientists don’t just need another model. They need the infrastructure that lets any scientist access models securely, efficiently, and at scale.
Tamarind Bio makes state-of-the-art molecular AI models usable out of the box, with no ML or infrastructure expertise required, so scientists can focus on drug discovery and research.
Right out of the gate, we’ve seen massive demand from customers. With intense need for this capability, Tamarind has now rapidly grown from its humble beginnings solving this pain point for a single Stanford Lab to become the trusted platform for molecular AI inference, supporting 8 of the top 20 pharma, hundreds of biotechs and academic institutions, and tens of thousands of scientists.
Since last year, we’ve grown 700% and welcomed leading pharma and biotech customers such as Bayer, Boehringer Ingelheim, Adimab, Mammoth Biosciences, and Flagship Pioneering to build on our platform.
“With AI becoming a core part of the drug discovery R&D workflow, we started Tamarind two years ago to enable any scientist to design drug candidates, without thinking about cloud, data or AI inference infrastructure.” says Deniz Kavi, co-founder and CEO of Tamarind Bio. “Instead of building internal teams to optimize models as they come out, taking up much of the data science organization’s internal facing software, we allow scientists to focus on discovering novel biology, not repetitive infrastructure work.”
As Dimension Capital Co-Founder and Managing Partner Nan Li says, “Much like other sectors in the economy, increasingly powerful AI tools are spreading into core workflows in life science. Computational tools are steadily eating the wet lab and BioPharma accesses compute through Tamarind. We are excited to support Tamarind in their journey to build the leading ML orchestration and inference cloud for life science.”
Our original inference product for models like AlphaFold has expanded to include proprietary enterprise models, training, and multi-stage scalable pipelines. Our library of AI models has grown to hundreds, not just open-source tools, but also our users’ internal protocols, proprietary models trained on users’ own data, pipelines of multiple models together and more!
Now, we are doubling down on our commitment to building the core AI and data infrastructure to power the next generation of medicines. We will continue to support open models, strengthening them with proprietary data, and prioritize access to all scientists.
Join us as we build the infrastructure for all AI-powered drug discovery - Careers, LinkedIn