Neural Network Compression
Shrink your AI models.
Keep the accuracy.
Domain-aware compression for satellite imagery, medical imaging, and industrial inspection. 5–10x smaller models that preserve the features generic compression destroys.
The problem
Large models can’t deploy at the edge
- ✗GPU inference costs eat into margins — $0.50+ per 1,000 satellite tiles on cloud GPUs.
- ✗Latency kills real-time applications. 200ms round-trips don’t work for production-line QA or autonomous systems.
- ✗Generic compression (uniform pruning, post-training quantization) destroys the subtle features that matter in specialized domains.
Our approach
Compression tuned for your domain
- ✓Domain-aware saliency analysis identifies which weights, channels, and precision levels drive performance on your data.
- ✓Compress aggressively where safe, preserve fidelity where it matters. 5–10x smaller with under 2% accuracy loss.
- ✓Deploy on edge GPUs, NPUs, and FPGAs — run inference locally without cloud dependencies.
Use Cases
Built for high-stakes vision
Satellite Imagery
Geospatial Intelligence & Remote Sensing
Compress multispectral and SAR models for real-time satellite analysis. Preserve sub-pixel object detection and spectral feature discrimination across 16+ band imagery.
10x compression on YOLO-RS with <1.5% mAP loss
Medical Imaging
Radiology, Pathology & Diagnostics
Maintain diagnostic accuracy in compressed CT, MRI, and histopathology models. Our domain-aware approach protects the high-frequency features critical to medical diagnosis.
8x compression on UNet-3D with 99.1% Dice retention
Industrial Inspection
Manufacturing QA & Defect Detection
Deploy compressed vision models directly on production lines. Detect micro-fractures, surface anomalies, and assembly defects at wire speed on edge GPUs and NPUs.
6x compression, 3ms latency on Jetson Orin
How It Works
Three stages of intelligent compression
Structured Pruning
We analyze activation patterns on your domain data to identify redundant filters and attention heads. Unlike magnitude pruning, our approach uses domain-specific saliency maps to remove only structures that don't contribute to your task.
Mixed-Precision Quantization
Each layer gets a precision level matched to its sensitivity on your data. Critical feature extraction layers stay at higher precision while redundant layers are aggressively quantized — often to INT4 — without measurable accuracy impact.
Domain-Tuned Distillation
A compact student model learns from the full-size teacher using your domain data distribution. We transfer not just output logits but intermediate feature representations that matter for your specific task, ensuring the compressed model retains domain expertise.
Pricing
Start compressing today
One model, one flat fee. No per-inference charges, no cloud lock-in. You own the compressed model.
Starter
- ✓ Single model compression
- ✓ Up to 5x compression target
- ✓ Standard domain calibration
- ✓ ONNX & TensorRT export
- ✓ Accuracy benchmark report
Enterprise
- ✓ Multi-model pipeline compression
- ✓ 10x+ compression targets
- ✓ Custom domain calibration
- ✓ On-premise deployment support
- ✓ Dedicated engineering support
Ready to deploy smaller, faster models?
Tell us about your model and domain. We’ll show you exactly how much we can compress it — and what accuracy you’ll keep.