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EI
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EI
Edge AI runtime
// 01 — Edge Runtime · Stable

Perception, inference, and dispatch run on-device, sub-100ms, inside the cameras and sensors already mounted to the floor.

Inference
01
On-device
Latency
02
Sub-100ms
Install
03
On request
The stack

Three layers of one working runtime.

Sensors feed inference. Inference feeds dispatch. Each layer ships on the same node, beside the camera.

01 · Edge compute

Inference at the source.

Vision and sensor models execute on the node beside the camera. Sub-100ms decisions, no cloud round-trip, offline through outages.

  • Jetson nodes
  • Custom edge runtime
  • Offline-resilient cluster
Layer 01Online
02 · Perception

Models that read the environment.

Detect, track, segment, and fuse signals from every camera into one spatial picture. Privacy boundaries enforced at the frame.

  • Multi-camera fusion
  • Behavior and anomaly detection
  • ONNX runtime
Layer 02Online
03 · Dispatch

Perception wired into operations.

From a tracked event to a dispatched response in one hop. Webhooks, building systems, and ticket queues consume the same stream.

  • Event webhooks
  • Building-system actuation handlers
  • Kafka
Layer 03Online
Environments

Six environments, one runtime.

Each environment names a real operation: a queue, a fall, an elevator door, a loading bay. The runtime is the same node.

Health

Hospitals

Fall detection, hand-hygiene compliance, bed turnover, and patient flow across wards, corridors, and triage.

// 01 · pilot-readyLive concept
Retail

Retail floors

Queue length, shrink events, dwell heatmaps, and shelf gaps tracked without storing identifiable footage.

// 02 · pilot-readyLive concept
Buildings

Building systems

Occupancy, HVAC load, access events, and incident alerts fused into a single floor-by-floor telemetry stream.

// 03 · pilot-readyLive concept
Vertical

Smart elevators

Door-zone safety, predictive maintenance, and dispatch logic tuned per car, per shift, per building.

// 04 · pilot-readyLive concept
Public

Public spaces

Crowd density, anomaly flags, and incident triage across stations, plazas, and transit corridors.

// 05 · pilot-readyLive concept
Industry

Industrial sites

Defect catches, PPE checks, and throughput telemetry at the line, the bay, and the loading dock.

// 06 · pilot-readyLive concept
Primitives

Six primitives, named for the runbook.

Each primitive is the term a deployment engineer would type. Composed per site.

C/01 · Capability

Detect

People, vehicles, objects, behaviors — at frame rate.

realtimeedge-native
C/02 · Capability

Analyze

Streams roll up into dwell, density, and throughput.

realtimeedge-native
C/03 · Capability

Fuse

Cameras, lidar, and IoT into one signal.

realtimeedge-native
C/04 · Capability

Twin

Live state mirrors the site, zone by zone.

realtimeedge-native
C/05 · Capability

Edge

On-device weights tuned for latency and outage.

realtimeedge-native
C/06 · Capability

Dispatch

Events route to webhooks, tickets, and shifts.

realtimeedge-native
Vision · 2026

The next layer of computing runs inside the rooms people already occupy: hospital wards, supermarket aisles, factory lines, elevator shafts, station platforms. Cameras and sensors are the keyboards.

Edge Intelligence is the runtime for that layer. Perception on the node, dispatch on the wire, telemetry on a stream — one substrate across every deployment.

The screen was the last interface. The environment is the next interface.

Shipping now · install on demand
Install

One install, one site.

If you run a hospital ward, a retail floor, a vertical-transport fleet, or a logistics bay and have cameras already mounted, we install the runtime.

Site installsResearch collaborationsInfrastructure partners