Automate
Without Coding
Science-native autonomous workflows with open source orchestrator and drivers.
Trusted by or Partnered with
IvoryOS Core
Develop in Python? IvoryOS turns it into an autonomous experiment platform in one line.
import ivoryos
class MySelfDrivingLab:
def add_liquid(self, amount: float, ...):
"""Add reagent"""
...
def analyze(self):
"""Analyze data"""
return 1
robot = MySelfDrivingLab()
Workflow Builder
Design experiments with visual drag-and-drop interface
Instant Web Interface
Research changes faster than software cycles. IvoryOS turns evolving Python code into an instant web interface — so scientists can test immediately while developers refactor freely.
Science-Native
Designed for experiments — iteration, parameter exploration, and adaptive optimization.
Composible workflows
Pivot faster by rebuilding workflows from reusable building blocks — no rewrites required.
AI Intelligence
Interact with experiments in natural language — query data, guide decisions, stay in the loop.
Reproducible
Every action is logged. Standardized execution ensures results are repeatable.
IvoryOS Hub
Go to Hub (beta)Build a platform from scratch — Discover, install, and launch your self-driving lab.
1Discover
Browse the community library for hardware drivers.
2Install
Install drivers and IvoryOS Core to your lab.
3Build
Deploy your configuration and start running automated experiments.
Why IvoryOS?
The journey to autonomous discovery and how we're changing the future of lab automation.
Why automate scientific research?
Automation isn’t just about saving time or improving reproducibility. It enables autonomous discovery — systems that can run, learn, and decide what to try next. The goal isn’t faster experiments — it’s faster discovery.
Why is flexibility critical in R&D automation?
R&D is unpredictable. Workflows constantly change, and trial-and-error is essential. Scientists need to modify workflows quickly and add new instruments or steps easily. Rigid systems slow down discovery — flexibility isn’t optional, it’s essential.
Why is coordinating multiple instruments so hard?
Modern labs have many systems — robots, analytical tools, software — that don’t naturally communicate. Existing software is usually closed, hardware-specific, and rigid, so there’s no solution for dynamic, multi-instrument workflows.
Why is programming still the best solution today?
Programming remains the most flexible, powerful, and scalable way to automate scientific research. However, most scientists are not trained to code, and even if they were, it would be a waste of their time and talent.
Why not just hire more engineers?
The bottleneck is collaboration, not execution. Communication takes time, iterations slow down, and small changes become expensive. Scientists need independence in experimental design.
How does IvoryOS solve this?
IvoryOS combines an open, plug-and-play core (connect any instrument), flexible interface (visual + programmable), and an ecosystem layer (share and collaborate). It turns experiments into something programmable, composable, and scalable.
Why does an ecosystem matter?
The problem isn’t just tools — it’s fragmentation. An ecosystem allows sharing workflows, reusing integrations, and connecting people. Instead of retraining everyone, you connect and build on each other.
Who is IvoryOS for?
IvoryOS is for labs building automation from scratch, teams scaling complex workflows, researchers who want more control, and manufacturers with dynamic processes.
Can I use IvoryOS for beyond science, like on coffee-making robots?
Technically yes — but it’s overkill. IvoryOS shines when workflows are dynamic, iterative, and decision-driven. If your goal is to autonomously optimize the best ingredient and brewing method combination, that’s exactly what we can do.
Meet the Team
IvoryOS initiated in 2023 at the University of British Columbia (Hein Lab).
Ivory Zhang
Founder & CEO
Jason Hein
Scientific Advisor
Fergus Klein
Business Advisor
With support from
