Build ML models without code locally.

Clean data, train a model, and validate it, no code, nothing sent to the cloud. Built for ML teams of any kind. No wifi needed.

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 sensitive or proprietary data, that's often not an option under compliance or privacy requirements.

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

Revise solves ML for all data types.

Tabular, imaging, and signaling workflows are in active beta. NLP studio is 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.

In beta Watch demo â–¶
Imaging workflow

Imaging Studio

Classification, segmentation, and detection on image sets, from photos to DICOM files.

In beta Watch demo â–¶
Signaling workflow

Signaling Studio

Sensor data, biosignals, and time-series modeling workflows.

In beta Watch demo â–¶
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 multiple models per session

Compare multiple 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.

0 Models built
0 Users
0 Labs

Choose Revise. AI should be simple. Test the beta for free and create a machine learning model without writing a line of code.

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