Build health AI models without writing code.
Clean data, train a model, and validate it, no code, nothing sent to the cloud. Built for health AI teams of any kind, not just academic labs.
Without Revise
~400 lines of code
2 days start to finish
1 models trained
A typical modeling workflow, before and after Revise
Watch the workflow
One workflow solves everything
A quick look at a tabular-data workflow, from raw data to trained model.
The gap
The gap isn't ambition. It's access.
Speed, privacy, and skill each block teams with a real dataset from getting to a working model.
Speed
Weeks, not minutes
Even a simple model can take weeks once you count learning a library, debugging, and waiting on someone else's schedule. That's an efficiency problem, not a science problem.
Cloud-based
Most tools assume the cloud
Leading AutoML platforms are built cloud-first. For clinical, biomarker, and survey data, that's often not an option under IRB or HIPAA terms.
Skills gap
The tools assume you can code
Plenty of people have a real dataset and a real question, just not the programming background most ML tools were built for.
How it works
One workflow for any health AI team.
Tabular and imaging workflows are live today. Signaling and NLP studios are in development.
Tabular workflow
Dataset to model
Upload a spreadsheet, clean it, train a model, and validate it, all through a guided interface. No scripts.
Available now
Imaging workflow
Imaging Studio
Classification, segmentation, and detection on image sets and DICOM files.
Available now
Signaling workflow
Signaling Studio
Biosignals, omics, and time-series modeling workflows.
In development
Text / NLP workflow
NLP Studio
Text classification, named entity recognition, and LLM fine-tuning workflows.
In development
What's included
Everything between raw data and a working model
Every workflow includes the same core toolkit, not just training.
Data cleaning
Fix your data first
Handle missing values, outliers, and formatting issues through a guided interface before you ever train a model.
Visualizations
See your data, not just numbers
Built-in charts and distributions help you understand your dataset before and after cleaning, no plotting library required.
Model training
Train up to 8 models per session
Compare up to 8 models in parallel in a single session, so you can pick the best performer instead of training one at a time.
Save & share
Host, save, and send models
Every trained model is saved automatically. Host it locally, or upload and send it to a collaborator directly through the app.
Currently used at two of the top medical institutions in California, teams are training real models on real clinical datasets with Revise.
Choose Revise. AI should be simple. Create a machine learning model without writing a line of code, starting today.
Try Revise on your own data
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