Better Models.
Less Data.

Get more performance from the data you already have.

Run Compatibility Check Read Case Studies

Help models learn more from every training example.

AI teams face three ways to improve model performance.

When model accuracy is not good enough, most teams have three options:

Collect more data

Gather additional labeled examples to improve model learning.

Build larger models

Increase model capacity and parameter count.

Help models learn more efficiently

Extract more value from existing training data.

Most teams focus on the first two. Perforated focuses on the third.

By adding additional learning signals during training, Perforated helps models extract more value from the data they already have.

More accuracy. Fewer examples. Smaller models.

BUILT FOR PYTORCH

Help models learn more from every training example.

Perforated integrates into existing PyTorch workflows and adds neuron-specific learning signals during training. These signals help models learn more efficiently from available data, improving accuracy without requiring larger datasets or larger models.

Keep your existing model architecture
Test against your current validation metrics
Continue using your existing deployment pipeline

model = perforate_model(model)

while not training_complete:
    train_one_epoch()
    score = validate()
    model, training_complete = pai_tracker.add_validation_score(
        score, model
    )

Representative integration pattern. Exact setup depends on model architecture, dataset, and evaluation metric.

Built for Data-Efficient Training

Designed for ML teams building production AI systems under real-world constraints.

Train With Less Data

Reach target performance with fewer labeled examples.

Improve Existing Models

Extract more value from datasets you already have.

Reduce Annotation Costs

Decrease dependence on expensive labeling cycles.

Build Smaller Models

Achieve strong performance with fewer parameters.

Deploy Efficiently

Carry training gains into production environments.

Integrate Quickly

Works with existing PyTorch workflows.

Get more performance from the data you already have.

Perforated helps teams improve model accuracy through data-efficient training without requiring larger datasets or forcing a model rebuild.

Preserve production workflows

Train with fewer labeled examples

Evaluate against your existing benchmarks

Run an Evaluation

Built for production ML

Integrates Into Existing ML Workflows.

Compatible with existing PyTorch pipelines and designed for fast evaluation inside real-world ML environments. Perforated helps teams improve model performance without changing architectures, redesigning workflows, or adjusting deployment infrastructure.

Minimal code changes for existing PyTorch workflows
Test against existing models, datasets, and benchmarks
Compatible with modern optimization techniques like quantization, pruning, and distillation

Proven In Production.

Up to 50%
Less Training Data: Up to 70%
Performance Improvement: Up to 40%
Faster Iteration Cycles: Up to 97%
Lower Deployment Cost

What teams are seeing

"We needed to improve model performance but couldn't afford to label more data. Perforated helped us get better results from what we already had."

ML Engineering Lead

Large Enterprise AI Team

"Integration was surprisingly lightweight. We tested against existing PyTorch models and benchmarks within hours."

Senior Applied AI Engineer

Mid-Stage AI Infrastructure Company

"The efficiency-to-accuracy tradeoff was the biggest surprise. We got better models without collecting more training data."

Computer Vision Team Lead

Autonomous Systems Company

Common Questions

How Perforated integrates into existing ML workflows and helps teams train more efficient models.

Do we need to rebuild our models to use Perforated?

No. Perforated is designed to integrate into existing PyTorch workflows with minimal code changes and without requiring architecture rebuilds.

What types of models work best with Perforated?

Perforated has shown strong results across computer vision, tabular models, time series, and smaller language model workflows, especially in data-constrained environments.

Is Perforated a data labeling or synthetic data platform?

No. Perforated is not a data labeling platform. It helps models learn more efficiently from the labeled data teams already have by adding neuron-specific learning signals during training.

How long does integration typically take?

Most teams can begin testing Perforated against existing models and benchmarks within hours using existing PyTorch pipelines.

What kinds of improvements should teams expect?

Results vary by workload, but teams commonly evaluate Perforated for higher model performance with fewer labeled examples, smaller models, reduced annotation costs, and more efficient deployment.

Can Perforated work alongside quantization or pruning?

Yes. Perforated works during training and is compatible with existing optimization techniques including quantization, pruning, distillation, and fine-tuning workflows.