Edge AI · IoT & Robotics · Autonomous Systems

Offline IoT & Robotics

Sovereign AI inference for robots, autonomous systems, and IoT deployments. Runs entirely on your edge hardware with persistent mission memory and zero cloud dependency.

Built for air-gapped, contested, and denied environments.

0bytes

sent to the cloud

100%

air-gap capable

edge queries, no metering

Real-time

on-device inference

The Case for Edge AI

Real constraints.
Resolved at the edge.

01

Connectivity

"Our robots lose connectivity in the field and the AI just stops working."

Zero connectivity required. All inference runs on-device from first boot to mission complete.

02

Data Sovereignty

"Sending sensor data and telemetry to the cloud violates our data sovereignty policy."

All sensor processing and AI reasoning stays on-device. Zero external transmission by architecture.

03

Latency

"Cloud latency makes real-time autonomous decisions impossible."

Sub-millisecond local inference. No round-trip. Decisions happen at the sensor.

04

Cost at Scale

"We pay per API call for AI features — costs are unpredictable at scale."

Perpetual edge runtime. Unlimited inference. One flat cost regardless of fleet size or query volume.

License

Perpetual Edge Runtime

Optional Annual

Support & Model Refreshes

Zero Cloud Dependency·On-Device Inference·Air-Gap Capable·Perpetual License · Annual Support Available·Real-Time Edge Decisions·CMMC Aligned·Your Data. Your Hardware. Your Mission.·Zero Cloud Dependency·On-Device Inference·Air-Gap Capable·Perpetual License · Annual Support Available·Real-Time Edge Decisions·CMMC Aligned·Your Data. Your Hardware. Your Mission.·

Mission Memory

Operational knowledge that compounds across missions

Every mission run, sensor event, and decision log adds to a persistent, queryable memory layer stored entirely on-device.

Mission Memory Index

Fleet Accumulated Operational Knowledge

On-Device

4,821

Mission events logged

312

Missions completed

98

Anomalies detected

0

Bytes transmitted externally

Knowledge compounds with every mission. Zero bytes ever leave your hardware.

Offline IoT AI eliminates connectivity risk through edge-native architecture. No network means no exposure, no latency, no failure mode.

01

Edge-Native Inference

Runs entirely on your embedded hardware — x86, ARM64, NVIDIA Jetson, Raspberry Pi, and custom SoCs. No connectivity required for any inference operation.

llama.cpp · x86 · ARM64 · Jetson · RISC-V · GPU offload

02

Autonomous Decision Making

Deploy AI reasoning loops that operate without a cloud backend. Robots and IoT systems make local decisions using on-device models with persistent context.

Closed-loop control · Local reasoning · Zero-latency decisions · Sensor fusion

03

Persistent Mission Memory

Every mission run, sensor event, and operational log contributes to a queryable memory layer stored entirely on the device. Context compounds across deployments.

SQLite · On-device vector store · Cross-mission recall · Structured event logs

04

Multi-Modal Sensor Intelligence

Process vision, lidar, telemetry, and structured sensor streams through a unified AI layer. No data leaves the device boundary.

Vision · LiDAR · IMU · CAN bus · MQTT · Custom sensor APIs

05

Air-Gap & Contested Environment Ready

Designed for denied, degraded, and disconnected environments. Operates with zero network dependency from first boot to mission completion.

No connectivity required · CMMC-aligned · D3 environment tested · Secure boot

06

Fleet Telemetry & Reporting

Aggregate operational telemetry and AI decision logs locally. Generate structured mission reports exportable to your ground control system.

JSON · CSV · MQTT export · Ground station sync · Offline-first logging

Mission Workflow

From deployment to debrief, entirely on-device.

1

Configure mission context

Launch Offline IoT AI → New Mission → set objective, environment type, and sensor configuration. All stored locally.

2

Load your model

Select or download an open-source model suited to your hardware. ARM64, Jetson, or x86 — hardware-aware recommendations auto-applied.

3

Deploy and run

The AI inference loop starts on-device. Sensor streams feed directly into the local model. Zero network calls during operation.

4

Real-time decisions

The system reasons on live sensor data and executes autonomous decisions — obstacle avoidance, anomaly flagging, route correction — all locally.

5

Debrief and export

Mission complete — generate a structured report from the on-device log. Export as JSON or PDF for ground control or compliance records.

Defense & Military Robotics

Industrial Automation Teams

Autonomous Vehicle Programs

Critical Infrastructure Operators

Agricultural Robotics Teams

Aerospace & UAV Programs

You choose the model.
It runs on your hardware.

Browse and download open-source AI models directly inside the app. Switch models between missions to match the task — a lightweight model for real-time control, a larger one for post-mission analysis. Once downloaded, zero internet required.

Supports x86, ARM64, NVIDIA Jetson, Raspberry Pi, and custom SoCs
Hardware-aware model recommendations for your CPU, GPU, and RAM
Switch models without interrupting the deployment
Download from HuggingFace and curated edge model registries

Sovereign edge intelligence.
Starting today.

Deploy the Offline IoT AI runtime on your hardware and run your first mission in minutes. No account required. No data transmitted.

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