Profluent Bio vs Emulate
Side-by-side comparison of AI visibility scores, market position, and capabilities
AI protein design company released OpenCRISPR-1, the first gene editor designed entirely by AI; raised $35M developing novel enzymes, antibodies, and gene-editing tools by exploring protein sequence space beyond what evolution has produced.
About
Profluent Bio is an AI protein design company founded in 2022 by former Meta AI researcher Ali Madani, raising $35M in seed and Series A funding. The company trains large generative models on biological sequence data to design novel proteins with specific structural and functional properties, analogous to how large language models generate text. Profluent made scientific history in 2024 by publishing OpenCRISPR-1, the first gene editor designed entirely by AI rather than adapted from natural organisms, which the company released open source for non-commercial research. The platform enables design of novel enzymes, antibodies, and gene-editing tools by exploring protein sequence space far beyond what evolution has produced. Profluent works with pharmaceutical and agricultural biotechnology companies that need custom protein tools for research and therapeutic development. The company represents a fundamental advance in moving protein engineering from directed evolution and rational design toward generative AI-driven discovery.
Pioneering organ-on-chip platform installed in 150+ labs including 17 of the top 25 biopharma companies; launched AVA™ high-throughput 96-chip system in 2025 and holds the only FDA ISTAND-accepted DILI Organ-Chip.
About
Emulate, Inc. is a Boston-based biotechnology company that designs, manufactures, and commercializes Organ-Chip technology — micro-engineered devices lined with living human cells that replicate the microstructure and function of human organs. Founded in 2013 as a spin-out from the Wyss Institute at Harvard University, Emulate's chips model the liver, intestine, lung, kidney, brain, and other organ systems with a fidelity that animal models and traditional cell culture cannot match. The technology was originally developed to reduce drug development attrition rates caused by poor translation from animal studies to human clinical outcomes.
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