We partner with biotech teams across the full lifecycle of biological research — from early hypothesis validation to production-scale analysis and lab IT.
Most biotech teams fail not because the biology was impossible — but because early assumptions were never pressure-tested, infrastructure was treated as an afterthought, and in-silico work and wet-lab work were disconnected.
We believe uncertainty should be reduced as early as possible, failed ideas should fail cheaply and computationally, and early validation and long-term infrastructure should be the same work.
We don't sell software products. We partner with you to reduce uncertainty, build scalable systems, and design infrastructure that evolves with your science.
Design computational experiments to test biological ideas. Simulate and model biological systems. Perform large-scale sequence and data analysis. Prioritize experimental paths before wet-lab work.
Design reproducible workflows that scale with real datasets. Build pipelines that survive real usage, not just demos. Implement versioning, logging, and traceability.
Design cloud environments for labs. Set up compute, storage, access control, and security. Ensure infrastructure matches scientific workflows and reduces friction for researchers.
Apply ML to prioritization, prediction, and filtering. Integrate models into real pipelines. Focus on interpretability and decision support, not complexity for its own sake.
We don't start with tools or platforms. We start with your scientific questions, experimental decisions, and constraints.
Understand your scientific questions, constraints, and what decisions you're trying to make.
Work closely with your team to build systems that evolve with your understanding.
Make decisions transparent. Avoid unnecessary complexity. Keep focus on what matters.
Continue as your systems evolve. Infrastructure adapts to science, not the other way around.
Tell us about your research and challenges. We'll respond within 24 hours.