TRUSTED BY LEADING ORGANIZATIONS
Real Numbers, Real Deployments
125
X
Faster Inference
MODEL
HTCNN
DEPLOYMENT
STM32H747 MCU
RESULT
300s → 2.4s inference time
70
%
Memory Reduction
MODEL
Solar-31B / Multiple CV models
DEPLOYMENT
LPU / Server · NPU
RESULT
61.8 GB → ~19 GB / 60%+ size reduction
50
%
Inference Cost Reduction
MODEL
MoE LLM (Solar, Qwen3)
DEPLOYMENT
GPU Server (A100)
RESULT
GPU 4 → 2 units required
Solve Every Deployment Challenge
with One Platform
Turn deployment challenges into deployable results.
01
Not Running on Target Device
Architecture incompatibility blocks deployment
02
Unusable Performance
Models too slow for real-world use
03
Fragmented Workflow
Scattered toolchains create integration overhead
04
No Visibility Before Deployment
No way to validate performance before shipping
05
Rising Infrastructure Cost
GPU sprawl drives runaway inference expenses
All of these, solved by
A unified platform to deploy any AI model on any device — reliably, efficiently, at scale.
PROFESSIONAL SERVICE
Need Help? We've Got You Covered
When optimization becomes complex, our team ensures your models run successfully on your target device.
Edge AI Optimization
Expert-led model compression and hardware adaptation for edge devices including MCUs, mobile SoCs, and embedded platforms.
NPU Optimization
Deep compatibility work to make vision models and LLMs run on diverse NPU architectures with validated performance guarantees.
LLM Optimization
Specialize large language models for production — reduce GPU footprint, accelerate token throughput, and cut operational costs.
