Your AI. Your Hardware.
No Compromises.
We turn heavyweight neural networks into edge-ready models
Without sacrifycing accuracy
6x
FLOPS Reduction
84%
Energy Savings
74+
FPS on Edge Devices
GPU
Embedded
The Problem
The Edge Deployment Dilemma
You've built a neural network that works. But deploying it in embedded hardware means choosing between speed and performance.
Fast inference
Poor accuracy
High accurary
Too slow
Our Solution
Optimized Architecture
Custom model design tailored to your hardware constraints
Maximum Compression
Fast inference without sacrifycing accuracy
Insights
Automated Knowledge Distillation
We transfer the "intelligence" of large foundation models, which are built for powerful GPUs, into compact, edge-compatible architectures.
Foundation -> Edge
Multi-Objective Optimization
Our AutoML pipeline simultaneously optimizes for size, speed, and accuracy—finding the perfect balance for your use case.
SMAC + Hyperband
Automated Pruning
Intelligently remove redundant weights and connections while preserving model performance.
Structured Pruning
Quantization
Convert to INT8/FP16 for maximum hardware acceleration on edge devices.
Edge Device Ready