Built Technology for the Intelligent Edge
Liquid Neural Networks (LNN)
Inspired by continuous-time dynamical systems, our Liquid Neural Networks adapt in real-time to new data. Unlike static models, LNNs evolve over time, making them ideal for unpredictable, multi-modal edge environments.
xNN Multimodal Architecture
Our xNN framework processes simultaneous inputs like vision, audio, and sensor data. Each xNN layer is optimized for context-specific reasoning, making it ideal for complex edge deployments requiring real-time fusion.
Probabilistic Encoding for Efficient Computation
Rather than relying on deterministic outputs, our probabilistic encoding approach improves inference speed and robustness — especially in low-power or noisy environments.
Integrated Silicon Photonics
We embed silicon photonics directly into our edge platforms, enabling ultra-fast, low-latency data movement between sensing and AI inference layers. This dramatically reduces I/O bottlenecks while conserving power.
What Sets Our Platform Apart
• On-device learning with time-adaptive dynamics
• Native multimodal support for diverse input streams
• Optical interconnects for high-speed data handling
• Low-latency, low-power, privacy-first design
Our platform combines adaptive AI models, multimodal processing, and silicon photonics to deliver unmatched performance at the edge — with speed, efficiency, and autonomy.
