Now available for early access
Now available for early access
Now available for early access
Start exploring immediately with high-quality, anonymized datasets, and collaborate with other researchers.
Start exploring immediately with high-quality, anonymized datasets, and collaborate with other researchers.
Instant Access
Access patients' digital twins in minutes — no IRBs, DUAs, or lengthy approvals required.
Instant Access
Access patients' digital twins in minutes — no IRBs, DUAs, or lengthy approvals required.
Instant Access
Access patients' digital twins in minutes — no IRBs, DUAs, or lengthy approvals required.
Validate on Real Data
When you’re ready, securely validate your analysis on real patient-level data.
Validate on Real Data
When you’re ready, securely validate your analysis on real patient-level data.
Validate on Real Data
When you’re ready, securely validate your analysis on real patient-level data.
AI-Ready Environment
Develop and iterate in a cloud workspace preloaded with Python, R, SQL, etc.
AI-Ready Environment
Develop and iterate in a cloud workspace preloaded with Python, R, SQL, etc.
AI-Ready Environment
Develop and iterate in a cloud workspace preloaded with Python, R, SQL, etc.
Privacy-First by Design
Built with privacy validation, auditability, and enterprise-grade security from day one.
Privacy-First by Design
Built with privacy validation, auditability, and enterprise-grade security from day one.
Privacy-First by Design
Built with privacy validation, auditability, and enterprise-grade security from day one.
01
Start with Digital Twins
Access high-fidelity patient digital twins instantly, designed to mirror real-world populations without exposing patient identity.
01
Start with Digital Twins
Access high-fidelity patient digital twins instantly, designed to mirror real-world populations without exposing patient identity.
01
Start with Digital Twins
Access high-fidelity patient digital twins instantly, designed to mirror real-world populations without exposing patient identity.
02
Build & Iterate Freely
Explore data, test hypotheses, and train models in a scalable cloud environment built for healthcare research.
02
Build & Iterate Freely
Explore data, test hypotheses, and train models in a scalable cloud environment built for healthcare research.
02
Build & Iterate Freely
Explore data, test hypotheses, and train models in a scalable cloud environment built for healthcare research.
03
Validate on Real Data
Run your finalized code on real patient-level datasets in a controlled, privacy-preserving workflow.
03
Validate on Real Data
Run your finalized code on real patient-level datasets in a controlled, privacy-preserving workflow.
03
Validate on Real Data
Run your finalized code on real patient-level datasets in a controlled, privacy-preserving workflow.
Answers to common questions about setup, and how everything works.
How quickly can I start working with the data?
You can begin exploring anonymized simulated patient data immediately after signing up — no IRBs, DUAs, or legal reviews required. Everything is ready to go in a secure, pre-configured environment designed for fast onboarding and rapid iteration.
What kind of research can I do with simulated data?
Simulated data is ideal for exploratory analysis, hypothesis testing, model development, and validation workflows. It mirrors real-world patient distributions and care patterns, letting you build and refine methods before moving to actual patient-level datasets — all without compliance delays.
How does validation on real data work?
Once your analysis or model is ready, you can run it on our real-world de-identified patient datasets. We provide a controlled environment to execute your code securely, ensuring your findings are robust, reproducible, and grounded in actual patient data — without compromising privacy.
What programming languages and tools can I use?
You can work in Python, R, SQL, or SAS — all accessible through a Jupyter-based environment preconfigured for healthcare data science. Our platform runs on a scalable cloud backend with both CPU and GPU options, so you can go from analysis to model training without changing environments or waiting on infrastructure.
How quickly can I start working with the data?
You can begin exploring anonymized simulated patient data immediately after signing up — no IRBs, DUAs, or legal reviews required. Everything is ready to go in a secure, pre-configured environment designed for fast onboarding and rapid iteration.
What kind of research can I do with simulated data?
Simulated data is ideal for exploratory analysis, hypothesis testing, model development, and validation workflows. It mirrors real-world patient distributions and care patterns, letting you build and refine methods before moving to actual patient-level datasets — all without compliance delays.
How does validation on real data work?
Once your analysis or model is ready, you can run it on our real-world de-identified patient datasets. We provide a controlled environment to execute your code securely, ensuring your findings are robust, reproducible, and grounded in actual patient data — without compromising privacy.
What programming languages and tools can I use?
You can work in Python, R, SQL, or SAS — all accessible through a Jupyter-based environment preconfigured for healthcare data science. Our platform runs on a scalable cloud backend with both CPU and GPU options, so you can go from analysis to model training without changing environments or waiting on infrastructure.
How quickly can I start working with the data?
You can begin exploring anonymized simulated patient data immediately after signing up — no IRBs, DUAs, or legal reviews required. Everything is ready to go in a secure, pre-configured environment designed for fast onboarding and rapid iteration.
What kind of research can I do with simulated data?
Simulated data is ideal for exploratory analysis, hypothesis testing, model development, and validation workflows. It mirrors real-world patient distributions and care patterns, letting you build and refine methods before moving to actual patient-level datasets — all without compliance delays.
How does validation on real data work?
Once your analysis or model is ready, you can run it on our real-world de-identified patient datasets. We provide a controlled environment to execute your code securely, ensuring your findings are robust, reproducible, and grounded in actual patient data — without compromising privacy.
What programming languages and tools can I use?
You can work in Python, R, SQL, or SAS — all accessible through a Jupyter-based environment preconfigured for healthcare data science. Our platform runs on a scalable cloud backend with both CPU and GPU options, so you can go from analysis to model training without changing environments or waiting on infrastructure.
Work faster with instant simulated datasets, validate with real-world evidence, and develop in a secure, cloud-native environment — no delays, no red tape.
Work faster with instant simulated datasets, validate with real-world evidence, and develop in a secure, cloud-native environment — no delays, no red tape.
Work faster with instant simulated datasets, validate with real-world evidence, and develop in a secure, cloud-native environment — no delays, no red tape.