Autonomous Scientific Discovery at Scale
DiscoveryOS runs closed-loop AI workflows that autonomously design, execute, and learn from experiments. Compress years of research into months.
DiscoveryOS runs closed-loop AI workflows that autonomously design, execute, and learn from experiments. Compress years of research into months.
Drug discovery, materials science, and biotech research rely on hypothesis-testing loops that take months or years. Each experiment requires manual design, resource allocation, data interpretation, and refinement. Labs run 10-50 experiments per month. With DiscoveryOS, that becomes 500-5000 per month, with intelligence guiding each cycle.
You need an autonomous agent that understands your domain, learns from failures, and formulates better hypotheses with each iteration. DiscoveryOS is that agent.
DiscoveryOS is an autonomous research agent that operates in real-time with your lab systems. It does not replace scientists. It augments them by handling the mechanical parts of the discovery loop: design, execution, measurement, and iteration.
DiscoveryOS ingests your domain data, literature, and prior results. It uses multi-objective optimization to design experiments that maximize information gain, calibrated to your lab's constraints: budget, equipment, timeline.
The agent interfaces with your lab automation platform (Tecan, Labcyte, Opentrons, custom APIs). It orchestrates equipment, monitors real-time data streams, detects anomalies, and adapts execution on the fly.
As results stream in, DiscoveryOS runs causal inference, statistical validation, and domain modeling. It identifies signal, flags outliers, and generates hypotheses about what succeeded or failed and why.
The agent refines its understanding of your system with each experiment. It proposes the next batch design based on cumulative knowledge. Your team reviews and approves; the agent executes and learns.
Automatically balance competing goals: potency vs. selectivity, yield vs. purity, speed vs. cost. Navigate trade-off surfaces to find Pareto frontiers.
Native connectors for Tecan, Labcyte, Opentrons, and custom equipment. Write protocols, manage robotics state, and handle error recovery automatically.
Ingest papers, patents, internal data, and assay protocols. Build probabilistic models of your research space and reason about experiment likelihood.
Scientists stay in control. Agents propose experiments; humans review and approve. Feedback refines future designs iteratively.
Live dashboards show experiment status, data streams, alerts, and adaptive changes. No more manual log review or guesswork.
DiscoveryOS infers causal relationships in your system, explaining why experiments succeeded or failed, not just fitting curves.
Compress lead optimization cycles from 2 years to 6 months. Explore larger chemical spaces and find better molecules faster than traditional workflows.
Design polymers, ceramics, and composites autonomously. Navigate complex synthesis-property relationships without manual guessing.
Evolve enzymes and antibodies through autonomous directed evolution. Design mutations, predict effects, and iterate systematically.
Optimize crop genetics, soil formulations, and pest resistance. Run autonomous field trials with real-time weather integration.
$5,000/month
$15,000/month
Custom
Start with a free lab assessment. Our team profiles your current discovery process and estimates how much time DiscoveryOS can save you.
shippable in 4 to 6 weeks. Fermi mid-case has Year-1 ARR around $340K. Investment to production around $148K. probability of meaningful success around 7%, by Fermi heuristics.
Everything on this page. The brand, the score, the Fermi math, the audio pitch.
ICP, MVP scope, first 7 build tasks, 30/60/90 launch plan, GTM, email drip, LinkedIn message, objections, risk memo.
Unlock dossierDossier plus the working code starter, brand assets, copy library, and outreach pack.
See adopt scopeHire the team that built this to install, customize, and run launch with you.
See scopeThe Wishdeal Factory scores every idea against 10 Adoptability axes, separate from raw quality. Here are the numbers we surface for this one.