For biotech & biopharma
Agentic AI built for regulated, science-heavy work
Biotech and biopharma teams need speed without sacrificing rigor. Helix.AI is designed for exactly that balance: autonomous execution with a human firmly in the loop and provenance baked into every result.
The problem
A widening gap between data and answers
- Supply has outpaced demand capacity. Sequencing and other instruments generate data far faster than teams can analyze it.
- The people generating data aren't the people analyzing it. For roughly every computational biologist, about ten wet-lab scientists need data answers now.
- Exploration rarely reaches production. Custom, run-once workflows seldom become the scalable, repeatable pipelines teams depend on.
- Infrastructure is a barrier. Storing, moving, and processing large datasets requires computational setup most scientists shouldn't have to manage.
How we help
Agentic AI that closes the gap
Human-approved by design
Nothing runs until a scientist confirms the plan. Agentic autonomy with a deliberate approval gate — appropriate for regulated, high-stakes science.
Provenance you can defend
Every result ships with the exact code that produced it and a full-history bundle, so analyses are auditable and repeatable.
Right-sized infrastructure
Each run is routed to the most appropriate environment based on data size and cost — no over-provisioning, no manual ops.
Format-aware from upload
Biological file formats are recognized and profiled at upload, surfacing schema and assumptions before analysis.
Honest about uncertainty
The system surfaces assumptions and invalid-input issues rather than fabricating confidence — safe failure over misleading success.
Iterative review loops
Revise a workflow, change a plot, or add a data file mid-stream; downstream steps re-run coherently without losing history.
Who it's for
Helix.AI is built for computational biology and bioinformatics teams running RNA-seq, variant calling, single-cell analysis, and custom scientific workflows — where reproducibility, traceability, and review-and-correction loops matter as much as the result itself.