Phylo

Phylo is an applied AI research lab developing an Integrated Biology Environment (IBE) to unify and accelerate scientific discovery for biologists. The company was founded by Dr. Kexin Huang and Dr. Yuanhao (Jerry) Qu, who both recently earned PhDs from Stanford University. Huang's background is in computer science, focusing on AI for biomedical discovery with experience at Genentech and Pfizer, while Qu's expertise is in genetics, pathology, and CRISPR gene editing.

The company's core product, Biomni Lab, is a unified, agentic AI platform designed to address the fragmented nature of biological research. Scientists often spend significant time toggling between disparate software tools, databases, and analysis packages, which creates workflow inefficiencies. Biomni Lab acts as an orchestration layer, providing a central workspace with a chat-like interface where researchers can use natural language to execute complex tasks such as planning experiments, analyzing data, searching literature, and visualizing results like 3D molecular models. The platform integrates with hundreds of specialized tools and databases, using AI agents to automate data formatting and movement between them, significantly reducing manual work.

Phylo spun out of an open-source research project called Biomni that began at Stanford in June 2024. The project gained significant traction, being adopted by thousands of organizations, including major pharmaceutical companies. In early 2026, Phylo announced a $13.5 million seed funding round co-led by Andreessen Horowitz and Menlo Ventures' Anthology Fund to develop its commercial offering. The business model operates on a freemium basis; the core Biomni Lab is available as a free cloud service with usage limits, while a paid enterprise version offers dedicated infrastructure, custom agents, and enhanced security for larger organizations.

Keywords: Integrated Biology Environment, agentic AI, biomedical research, computational biology, workflow automation, scientific discovery, bioinformatics, AI for science, life sciences, drug discovery, data analysis, research platform, foundation models, multi-agent architecture, natural language commands, genetic engineering, molecular modeling, lab automation, pharma tech, biotech