BiotechOS is the first platform that makes biology a digital industry. Two people with a laptop can now run real preclinical programs across a global network of labs, get structured results back, feed them into AI, and iterate — the same way a startup ships software in the cloud today.
“Intelligence is becoming too cheap to meter. When hypothesis generation is free and experimental design is automated, the bottleneck isn't ideas — it's the physical world. Science is about to go exponential.”
Every other industry has been transformed by software. Drug discovery still runs on cold emails, PDFs, and spreadsheets. Studies start with weeks of manual contracting before a single experiment runs. Results return as Word documents with no structure and no connection to anything that came before. There is no operating layer. We are building it.
Thousands of CROs sit underutilized. Hundreds of biotechs need them. Today the relationship is built through cold emails and weeks of back-and-forth. We turn it into an instant digital transaction.
A biotech submits a study request the way a developer makes an API call. The platform routes it, monitors execution, ingests results in real time, and feeds them back into AI — automatically.
Big pharma's AI compounds on decades of proprietary data. Small biotechs alone can't compete. We change that with a cooperative — owners stay owners, but pool into one shared knowledge layer.
Programmable biology: a biotech places an experiment order the same way a developer provisions a server. Agentic workflows translate it into a precise protocol. Robots execute. Results return — fully formatted, ready for the next decision cycle. Order to result, no humans in the middle.
We launch the lights-out capability through Ginkgo's robotic infrastructure, then expand across the network as more CROs connect their instruments to the platform API — a distributed grid of automated biological compute nodes.
A decade ago, starting a software company meant buying servers and raising millions. Cloud computing collapsed that to a credit card and a weekend. We're building the same inflection for biology — and the people who get to invent medicine.
Frameworks for autonomous workflows that reliably translate intent into protocols, route them, and monitor execution — finally production-grade. The hard part is no longer the technology.
The FDA's framework explicitly opens the door to alternatives to animal testing — and to programmable, AI-driven regulatory pathways. The agency is now a partner, not a brake.
National momentum to modernize biomedical R&D infrastructure aligns with exactly what BiotechOS builds: a digital, observable, AI-native operating layer for drug discovery.
Each program flowing through BiotechOS generates regulatory documentation — pre-IND requests, FDA correspondence, study justifications, IND modules, GLP audit packages. Across hundreds of programs this becomes the largest structured dataset of preclinical regulatory documentation ever assembled.
The platform's regulatory AI learns from every interaction — what language FDA responds to, which study designs pass, which submissions require amendment and why. The most expensive bottleneck in drug development gets cheaper for everyone, every quarter.
A narrated tour of the live product — intake, prediction, routing, wetlab execution, and signed evidence packets. Three minutes. No slides.
BiotechOS is the first demand-aggregation and orchestration platform that treats laboratory infrastructure like a data center — eliminating the “Idle Tax” that consumes 60% of pre-clinical R&D budgets.
Stop overpaying for “idle air.”
Turn your lab from a boutique shop into a high-throughput factory.
A foundation-model layer for structure, ADMET, toxicity, dose response, and biomarker prediction. Every score ships with calibrated uncertainty and a full feature attribution.
An 11-stage state machine for any program: intake → design → predict → route → run → QC → analyze → report. Agents propose; humans approve. Every transition is signed.
Auto-route assays to the best CRO by capability, capacity, turnaround, and price — with bid-ask negotiation, signed SOWs, and live job telemetry from partner sites.
Closed-loop physical execution: protocol compilation, robot scheduling, plate-level QC, inventory consumption, and signed result return — running 24/7 without operator babysitting.
Bring a target, a hit list, or a hypothesis. Clarification agents shape it into a runnable program in minutes.
Models rank candidates with calibrated uncertainty. Designers iterate fragments, scaffolds, sequences.
Assays auto-route to in-house wetlab or the best CRO. Plates execute 24/7 with live telemetry.
Results return with a signed evidence packet. Approve, iterate, or hand off to downstream IND prep.
A single workspace surfaces predictions, runs, partners, and decisions — without leaving the OS.
We're talking with anchor CRO partners, design-partner biotechs, and a small cohort of investors. If any of those is you — let's meet.