Edge AI for the small scope.
Frontier Cloud LLM for the large.
Run quantized models on real hardware when latency, privacy, and offline matter. Connect securely to core Frontier Cloud LLMs when the job needs large context, deep reasoning, or business-isolated jailbox sessions.
Two compute planes. One product.
Edge handles small-scope, low-latency, offline work. Core Frontier Cloud LLMs handle large-scope reasoning — with secure, isolated business context when required.
Core Frontier Cloud LLM
Large-scope inference on frontier models (Google Gemini / Vertex, Alibaba Model Studio / Qwen). Secure cloud connection for deep reasoning, long context, and multi-step jobs the edge cannot hold.
Edge AI Runtime
Small-scope quantized inference on-device (INT8/INT4), offline-first on i.MX9-class, Jetson-class, and ARM Cortex-A targets — with optional escalate-to-cloud when scope grows.
Edge computation + Core Cloud computation.
Route each request to the right plane — keep private work local, scale up only when needed.
Edge AI computation
Small-scope tasks: local perception, short prompts, control loops, and offline continuity. Hardware-aware profiles for constrained silicon and predictable latency.
Core Cloud LLM computation
Large-scope tasks: long documents, multi-step planning, heavy reasoning, and frontier-model quality. Connected through a controlled cloud path — not an open public chat window.
Secure business jailbox
Isolated, tenant-specific cloud context for proprietary data and custom policies. Business knowledge stays in a sealed session boundary — not mixed into generic shared context.
Built for real hardware and real policy.
Firmware-grade release discipline applied to both edge packages and cloud connection policy.
Hardware-Aware Edge Runtime
Execution profiles tuned to target silicon, memory, and thermal envelope for small-scope on-device work.
Frontier Cloud Connection
Policy-controlled escalate path to core Frontier Cloud LLMs when scope exceeds the edge envelope.
Isolated Context Control
Custom jailbox contexts for business data — secure, auditable, and separated per tenant or workload.
Supported Hardware
Our runtime is engineered for constrained memory profiles and specialized edge accelerators.
i.MX9-class
Optimized for NXP i.MX9 series application processors, leveraging integrated neural processing units (NPUs) for low-power inference.
Jetson-class
High-throughput execution utilizing NVIDIA Jetson platforms, maximizing GPU-accelerated parallel processing.
ARM Cortex-A
Efficient execution on ARM Cortex-A CPU cores and integrated NPUs/GPUs, designed for constrained power envelopes.
2026 milestones
Quarterly product milestones for hybrid edge + frontier cloud delivery.
Business jailbox + design partners
Isolated business context sessions and early design-partner validation on real hardware fleets.
Frontier Cloud LLM connection
Secure escalate path to core cloud frontier models with evaluation and packaging pipelines.
Architecture lock + edge prototype
Hybrid architecture defined. Quantized edge inference validated on i.MX9-class targets.
Technical note: local LLMs on embedded devices
Our research note covers hybrid edge-cloud patterns, routing, quantization for i.MX9/Jetson-class targets, and secure cloud context isolation.
Edge-first hybrid patterns
When to stay on-device vs escalate to Frontier Cloud LLMs — with confidence and complexity routing.
Cloud → edge knowledge loop
Distill, benchmark, sign, and stage model packages like firmware — not live API dependence.
Business jailbox isolation
Tenant-isolated cloud sessions for proprietary context when large-scope work leaves the device.
Ready for hybrid edge + cloud AI?
Request a product brief on Edge Runtime, Frontier Cloud LLM connection, and secure business jailbox context.
Request product brief