AI is a human project.
Design and deploy production-ready neural networks — visually. With classical ML baselines when you need them. Classification, regression, and clustering. The exported project ships with a dataset scaffold so you bring your data and run training on your own machine.
Run it. Build it. Own it.
Design it. Export it. Own it.
Compose the model visually. Export a project that runs anywhere. No surrendering the code, configs, or artifacts.
Classification, regression, clustering.
Pick the right problem type. Supervised and unsupervised in the same project surface.
Neural networks plus classical ML.
Simple CNN, ResNet, MLP; logistic regression, linear regression, XGBoost, and K-Means.
Your data, your machine.
The exported zip ships a dataset/ folder shaped for your architecture. Drop your files in, run train.py, deploy from anywhere.
Dataset scaffolding
Exported zip contains a dataset/ folder shaped for your architecture: train/val/test for image classifiers, CSV slots for tabular, with a schema README.
Visual model designer
Pipeline graph with configurable preprocessing, model architecture, loss, optimizer, and metrics.
Code ownership
Export a clean PyTorch project with configs, training, evaluation, prediction, and model card.
Templates.
Start from neural network and classical ML templates that become inspectable projects, not hidden workflows.