About Avitai Bio

Making Biology Programmable, Predictable, and Accessible

Avitai Bio is building virtual cells — AI models that predict biological behavior in silico, making synthetic biology faster, cheaper, and predictive across scales.

Engineering Cells: Expensive and Slow

The DBTL cycle hasn't changed in decades — hundreds of iterations, 5-10% hit rates, and lab results that don't predict production scale.

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Development Timeline

Traditional cell line development takes 3-10 years per product

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Cost per Molecule

R&D costs $10-100M per molecule with trial-and-error approaches

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Experiment Success Rate

Only 5-10% of experiments produce commercially viable results

Our Approach

AI That Predicts Before You Experiment

Instead of running thousands of experiments hoping for a hit, our foundation models learn from biological data to predict outcomes, design optimal sequences, and navigate vast design spaces — reducing time and cost by orders of magnitude.

Without AI
  • Years of trial-and-error experimentation
  • Millions in wasted R&D budget
  • Low hit rates, unpredictable outcomes
  • Lab results fail at production scale
  • Knowledge siloed across teams
With Avitai Bio
  • AI-guided design reduces iterations
  • Fraction of traditional R&D costs
  • Significantly higher success rates
  • Physics-grounded scale-up predictions
  • Unified platform captures all knowledge

Our Journey

2024

Company Founded

Avitai Bio founded with the mission to make synthetic biology faster, cheaper, and predictable through AI-driven foundation models.

2024

Platform Development

Built four foundation models — Research, Dynamics, Central Dogma, and Perturbation — trained on diverse biological datasets.

2025

Open Source Launch

Released 7 open-source tools built on JAX/Flax NNX, contributing to the scientific ML community.

2025

Platform Beta

Opened early access to research partners, integrating AI-guided predictions into real synthetic biology workflows.

Our Values

Scientific Rigor

We ground our AI in biological principles and thermodynamic constraints. No black boxes — our models are interpretable and physics-informed.

Speed & Efficiency

We are obsessed with reducing iteration time from months to weeks, years to months. Every feature we build is measured by how much time it saves.

Open & Collaborative

We believe in open science. Our tools are open-source, and we collaborate with academia, industry, and the broader community.

Impact-Driven

We are building tools to solve some of the biggest challenges facing humanity: medicine, sustainability, food security, and bio-manufacturing.

Meet the Team

We're a team of ML engineers, software engineers, and computational biologists united by a mission to make synthetic biology accessible.

View Our Team

Join Us

We're hiring ML engineers, computational biologists, and software engineers who want to reshape how biology is engineered.

View Open Positions