Full-Stack Physical AI Platform
Build smarter and deploy faster to bring real-time intelligence to every device.
Intelligence at the Edge
Deploy neural networks directly on-device without the power, latency, or cloud dependency of traditional AI.
Real-time inference in constrained environments
Fast integration and scalable deployment
High-quality data capture at the edge
Why Full-Stack Matters
Syntiant brings together intelligent sensing, ultra-low-power neural processing and optimized AI models into a unified platform designed to simplify development and maximize performance. By optimizing every layer of the AI stack, customers can accelerate deployment while reducing power, cost and system complexity.
Faster development cycles
Lower power vs. general-purpose processors
Reduced BOM and system complexity
Reliable always-on performance
Built for Every Integration Path
Flexible integration across hardware, software, and sensors.
End-to-end full-stack
AI platform
Add inference to existing sensor systems
Integrated high-performance sensing
Edge AI Runtime
A lightweight runtime environment optimized for always-on AI applications.
Real-time inference with minimal CPU overhead
On-device multi-model orchestration
Event-driven
processing
Turnkey AI Software Models
Pre-optimized AI models across key modalities:
Talk, See, Hear and Wake & Classify
Designed for real-time, on-device performance
Optimized for low-power environments
Purpose-Built Neural Processing
At-memory neural decision processors designed for efficient edge AI.
Minimizes data movement
Maximizes compute efficiency
Reduces power and system cost
Up to 7.4× improvement in word detection
Up to 11.8× improvement in noise reduction
Significantly higher energy and MAC efficiency
Sensors for Physical AI
High-performance sensing for real-world environments.
Low power,
high SNR
Compact, embedded-ready designs
Reliable in demanding conditions
Ecosystem Integration
Built to integrate with existing systems.
MCU and SoC
compatible
Sensor and audio front-end support
No cloud dependency required
Key Use Cases
Consumer
Voice control, wearables, assistants
Industrial
Predictive maintenance, anomaly detection
Automotive
In-cabin voice, driver monitoring