For ML engineers · PyTorch native

Code without hallucinations

Four fidelities. One verified workspace. No AI inventing APIs that don't exist.

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Why the ACI

No hallucinated code

Every helper, every block compiles to verified PyTorch. The assistant suggests, you decide.

Move across fidelities

Begin with a template, add the helpers you need, and snap blocks together. Drop into full code whenever you want. It stays the same project the whole time.

One source of truth

Switch between F0 and F3 without losing a line. Your code evolves with you.

Built for the problem

ML code is different. Hallucinations train silently for hours. We solve that directly.

Build with verified libraries

PyTorch
GPU-accelerated tensor framework

Four fidelities, one ladder

F0
Templates

Verified project structures. Start on day one without boilerplate.

F1
Library

Curated PyTorch helpers. Nothing unverified enters your code.

F2
Blocks

Visual coding. Every block compiles to real, readable PyTorch.

F3
Script

Full control. Cell-based notebook for when you need the wheel.

The ACI in action

Conv2d(3, 64, 3)
BatchNorm2d(64)
ReLU()
MaxPool2d(2)
Flatten()
Linear(3136, 256)
ReLU()
Linear(256, 10)
train_epoch(model, loader, optimizer)
validate(model, loader)
get_optimizer(name, lr)
create_dataloader(path, batch_size)
ResNet50 Image Classification
VGG16 Feature Extraction
Custom CNN Architecture
Transformer Base Model
torch.optim.Adam()
torch.nn.CrossEntropyLoss()
torchvision.transforms.Compose()
torch.utils.data.DataLoader()
Generated PyTorch Code
import torch.nn as nn # Build your model with blocks model = nn.Sequential( nn.Conv2d(3, 64, kernel_size=3), nn.BatchNorm2d(64), nn.ReLU(inplace=True), nn.MaxPool2d(2), nn.Flatten(), nn.Linear(3136, 256), nn.ReLU(inplace=True), nn.Linear(256, 10) ) # All code is verified # No hallucinations, no guessing

Collaborate in real time, with your whole team building in the same project.

Every line is verified

From the library

No model guessing. Helpers come from a curated, verified collection. You know what you're pulling in.

Blocks compile clean

Every visual block generates readable, inspectable PyTorch. No black boxes.

You stay in control

The assistant explains, refactors, flags inefficiencies. It doesn't take the wheel.

One source of truth

Code stays canonical across all fidelities. No sync problems, no version conflict.

Want to see it in action?

A 20-minute walkthrough of how to build your first model. Our team will show you the four fidelities and answer your questions.

Book a demo →

Build on a verified codebase

No hallucinated imports. No reinventing wheels. Just focused prototyping.

Start coding free