Profluent

aid: profluent-bio
name: Profluent
description: Profluent is a Berkeley, California AI biology company building frontier protein language models and using them
  to design functional proteins for therapeutics, gene editing, agriculture, and industrial enzymes. Founded by Ali Madani
  — previously architect of the ProGen large language model for proteins at Salesforce AI Research — Profluent pairs in-house
  foundation models with a wet lab to author novel proteins from scratch and validate them experimentally. The company's research
  has been published in Nature Biotechnology and Nature, and it is best known for OpenCRISPR-1, which it describes as the
  world's first AI-designed gene editor, released freely for research and commercial use. Profluent also publishes the ProGen3
  family of protein generation models, ProseLM for structure-conditioned protein design, the E1 family of protein encoder
  models, Protein2PAM for CRISPR-Cas PAM specificity prediction, and the CRISPR-Cas Atlas dataset. Commercial partnerships
  include a multi-target collaboration with Eli Lilly worth up to $2.25 billion, and deals with Ensoma, Corteva, Integrated
  DNA Technologies, and the Rett Syndrome Research Trust. Profluent is backed by Spark Capital, Insight Partners, Air Street
  Capital, Altimeter, and Bezos Expeditions. The company does not publish a hosted developer REST API or commercial SDK; its
  surface area for external builders is the Profluent-AI GitHub organization (model weights and inference code), the Protein2PAM
  web tool, and partnership engagements.
type: Index
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source: []
  generated: '2026-07-22'
  method: derived
position: Provider
access: 3rd-Party
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/profluent-bio.png
tags:
- Artificial Intelligence
- Protein Design
- Protein Language Models
- Foundation Models
- Generative Biology
- Gene Editing
- CRISPR
- OpenCRISPR
- ProGen
- ProseLM
- Bioinformatics
- Therapeutics
- Agriculture
- Industrial Enzymes
- Open Source Models
url: https://raw.githubusercontent.com/api-evangelist/profluent-bio/refs/heads/main/apis.yml
created: '2026-05-24'
modified: '2026-05-24'
specificationVersion: '0.20'
apis: []
common:
- type: DomainSecurity
  url: security/profluent-bio-domain-security.yml
- type: Website
  url: https://www.profluent.bio
- type: Platform
  url: https://www.profluent.bio/platform
- type: Applications
  url: https://www.profluent.bio/applications
- type: Partnerships
  url: https://www.profluent.bio/partnerships
- type: Team
  url: https://www.profluent.bio/team
- type: Newsroom
  url: https://www.profluent.bio/media
- type: OpenCRISPR
  url: https://www.profluent.bio/modality/opencrispr
- type: Sandbox
  url: https://protein2pam.profluent.bio
- type: GitHubOrganization
  url: https://github.com/Profluent-AI
- type: SourceCode
  url: https://github.com/Profluent-AI/OpenCRISPR
  name: OpenCRISPR
- type: SourceCode
  url: https://github.com/Profluent-AI/progen3
  name: ProGen3
- type: SourceCode
  url: https://github.com/Profluent-AI/proseLM-public
  name: ProseLM
- type: SourceCode
  url: https://github.com/Profluent-AI/E1
  name: E1 Protein Encoder Models
- type: SourceCode
  url: https://github.com/Profluent-AI/Protein2PAM
  name: Protein2PAM
- type: SourceCode
  url: https://github.com/Profluent-AI/CRISPR-Cas-Atlas
  name: CRISPR-Cas Atlas
- type: Publication
  url: https://www.nature.com/articles/s41586-025-09298-z
  name: Design of highly functional genome editors by modelling CRISPR-Cas sequences (Nature, 2025)
- type: Publication
  url: https://www.nature.com/articles/s41587-024-02123-4
  name: Designing Proteins with Language Models (Nature Biotechnology, 2024)
- type: Publication
  url: https://www.nature.com/articles/s41587-022-01618-2
  name: Large Language Models Generate Functional Protein Sequences Across Diverse Families (Nature Biotechnology, 2023)
- type: Publication
  url: https://www.biorxiv.org/content/10.1101/2025.04.15.649055v2
  name: Scaling Unlocks Broader Generation and Deeper Functional Understanding of Proteins (bioRxiv)
- type: Careers
  url: https://job-boards.greenhouse.io/profluent
- type: Email
  url: mailto:info@profluent.bio
- type: Partnerships
  url: mailto:partnerships@profluent.bio
- type: LinkedIn
  url: https://www.linkedin.com/company/profluent-bio
- type: Twitter
  url: https://x.com/ProfluentBio
- type: YouTube
  url: https://www.youtube.com/@Profluent-Bio
- type: PrivacyPolicy
  url: https://www.profluent.bio/privacy
- type: TermsOfService
  url: https://www.profluent.bio/terms
- type: Blog
  url: https://www.profluent.bio/media
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com