Our thesis

In a world where biotech is exploding into a data powerhouse, betting on multi-omic big data fused with agentic AI isn't just smart—it's essential. This convergence is set to grab a skyrocketing share of the massive bioengineering space, with ArcellAI at the forefront, automating analytics and experiments to supercharge breakthroughs in life sciences and far beyond.

• $5.71T Biotechnology global market by 2034
• $762.73B Bioengineering technology market by 2032
• $190.98B Big data analytics in biotech by 2034
• $1.3T+ spent globally on data science labor annually, most of it on repetitive data tasks
• $3.1T in global productivity lost due to data science inefficiencies and infrastructure debt
• 80% of data science time is spent on wrangling, integration, and pipeline work
• 40% CAGR agentic ai in biotech enabling automated experimentation and genomic analysis
• 54% CAGR agentic ai in scientific discovery particularly in biotech for multi-omic integration and virtual simulations

What we’re building

We're crafting a game-changing AI platform that acts as the ultimate data scientist for multi-omics and beyond, automating complex analytics and experiments to fuel breakthroughs in biotech and engineering with unmatched speed and precision.

• Self-Driving Research Pipelines: Fully automated workflows that handle everything from raw multi-omic data to actionable insights, powered by a planner-executor-critic system for robust, repeatable results.
• Multi-Omic Powerhouse: Seamlessly integrates genomics, proteomics, metabolomics, and imaging data to drive discoveries in synthetic biology, enzyme design, and virtual cell modeling.
• Smart AI Reasoning: Agents equipped with biological knowledge graphs, foundation models, and experimental logic to propose hypotheses, design experiments, and interpret complex results
• Rock-Solid Reproducibility: Tracks every step, parameter, and decision with persistent memory, ensuring auditable, transparent science.
• Scalable Modular Design: Plug-and-play architecture adapts to biotech, materials science, precision fermentation, and more, with flexibility to tackle new domains.
• Scientist-Friendly Interface: Intuitive natural-language tools let researchers query, visualize, and steer intricate datasets without coding expertise.

Why it matters

• Traditional tools struggle with fragmented multi-omics and manual pipelines.
• ArcellAI transforms that by offering a reusable, self-driving data scientist across life sciences.
• The platform’s agentic design enables scaling from enzyme makers to virtual-cell researchers—and ultimately to any domain facing complex data and experimental cycles.
• 75-90% failure rate in general AI deployments in specialized domains like biomedicine. ArcellAI is equipped with the domain expertise guaranteeing successful deployments.

Traction & team

• LOI(s)
• Live prototype ("VC-GPT",) demoed publicly at calculus.house
• Founder previously built PyTDC, an ICML-published virtual cells AI platform with 30k MAU
• Signed regulatory counsel and founding engineer
• Term sheets received and confirmed interest from top accelerators and VCs
• Accelerator networks from Tampa Bay Wave CORE ELP, The Residency SF's calculus.house, and Peachscore.

About the founder

• built data & ml products accounting for $100M+ in revenue
• Open-sourced virtual cells AI platform with 30K+ MAU
• ICML-and-NeurIPS-published researcher in BioML
• Formerly SWE at Pinterest and AI researcher at Harvard
• MIT BS Computer Science
• Personal page for Alejandro 'Alex' Velez-Arce

Learn more

• calculus.house demo 1/3
• calculus.house demo 2/3
• Inventor's Residency SF (IRSF by The Residency) demo day
• Accelerator page (deck, fundraising, team, etc.)