Build Better Models.
Perforated helps ML teams improve model accuracy, reduce compute cost, shorten development timelines, and deploy more efficient AI systems.
Built for Real-World ML Constraints
Designed for ML teams balancing accuracy, compute cost, deployment constraints, and development speed in production AI systems.
Higher Model Accuracy
Improve prediction quality and recover lost performance without blowing up model size.
Lower Cloud Compute Costs
Reduce training and inference overhead while improving efficiency for cloud-based workloads.
Built For PyTorch
Integrates into existing workflows with minimal code changes, no deployment rebuilds, and coding-assistant support for setup in a single prompt.
Faster Deployment Cycles
Accelerate experimentation, shorten iteration timelines, and move models into production more quickly.
Less Training Data
Train effective models with fewer labeled examples and reduce dependence on large-scale datasets.
Edge-Ready Performance
Optimize on-device models for latency, memory, and power-constrained deployment environments.
Recover lost performance without changing your deployment path.
Perforated helps teams improve accuracy after compression, optimization, or edge deployment constraints without forcing a model rebuild.
Add Perforated to your existing training loop.
Perforated plugs into standard PyTorch workflows so teams can evaluate performance gains against their own models, datasets, and benchmarks.
- 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 and evaluation metric.
How We Fit Into Your Workflow
Perforated is designed to work alongside existing optimization and fine-tuning methods, helping teams improve accuracy, efficiency, and deployment outcomes without rebuilding their stack.
| Feature | Perforated Recommended | Compression (e.g., pruning, quantization) | Fine-Tuning |
|---|---|---|---|
| Reduce compute requirements | Strong | Limited | |
| Support edge deployment | Strong | Not designed | |
| Integrate into existing workflows | Rework needed | Workflow-specific | |
| Preserve quality under efficiency constraints | Accuracy tradeoffs | Resource-heavy | |
| Improve model accuracy | Accuracy loss | Sometimes | |
| Reduce training data needs | No impact | Sometimes |
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
Real infrastructure, measurable results.
Designed to improve model performance, reduce compute requirements, and accelerate workflows.
- Up to 97%
- Lower Inference Cost
- Up to 70%
- Remaining Error Reduction
- Up to 50%
- Less Training Data
- Up to 40%
- Faster Iteration Cycles
Smaller models with reduced
deployment overhead.
Recover performance lost
during compression.
Achieve target accuracy with
fewer labeled examples.
Shorten optimization and
deployment timelines.
What teams are seeing
"We were already quantizing and pruning, but accuracy loss was blocking deployment. Perforated recovered that performance."
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. Normally one improves at the expense of the other."
Computer Vision Team Lead
Autonomous Systems Company
Deployment Questions
How Perforated integrates into existing ML workflows, optimization pipelines, and production environments.
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 deployment-constrained environments.
Can Perforated work alongside quantization or pruning?
Yes. Perforated is designed to complement existing optimization techniques including quantization, pruning, distillation, and fine-tuning workflows.
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 accuracy improvement, reduced compute cost, smaller deployable models, reduced training data requirements, and faster iteration cycles.
Is Perforated focused on research or production use cases?
Both. The underlying technology is grounded in original research, but the platform is designed for practical deployment inside real-world AI workflows.
Improve Model Performance Without Rebuilding Your Stack
Evaluate Perforated against your existing models, workflows, and deployment constraints.