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Edge Intelligence

AI That Acts At the Source

Deploy production-grade AI models directly onto edge hardware. Millisecond inference, air-gap security, and offline resilience — without sacrificing centralized visibility.

Sub-10ms latencyNo cloud dependencyFleet-scale OTA
CLOUDModel Registry · Sync HubEDGE NODEInference · ActEDGE NODEInference · ActEDGE NODEInference · ActSensors · Cameras · PLCs · WearablesCLOUDEDGEDEVICE
<10ms
Inference Latency
99.9%
Uptime SLA
10K+
Edge Nodes Managed
0 bytes
Raw Data to Cloud

Built for the Harsh Reality of the Edge

Every capability is engineered for constrained hardware, unreliable connectivity, and zero-tolerance operational environments.

Sub-10ms Inference

Run quantized models directly on device. No round-trip latency — decisions happen at the source of data.

🔒

Air-Gap Secure

Sensitive data never leaves the premise. Full GDPR, HIPAA, and SOC 2 compliance without cloud exposure.

🌐

Offline-First Logic

Maintain full operational intelligence even when connectivity drops. Sync resumes automatically on reconnect.

🔄

Federated Model Updates

Push model improvements across your fleet without centralizing raw data. Continuous learning at the edge — privacy preserved.

📊

Edge Observability

Real-time telemetry, anomaly alerts, and performance dashboards across thousands of nodes from a single pane.

Industry Use Cases

From factory floors to field sites, Edge Intelligence delivers where cloud latency is not an option.

Manufacturing

Predictive Maintenance on the Shop Floor

Vibration sensors and thermal cameras feed local models that flag bearing failures 72 hours before they cause downtime — no cloud call required.

Healthcare

Patient Monitoring at the Bedside

Wearable vitals analysis runs on-device inside the ward. PHI never traverses the network; alerts reach nurses in under 200 ms.

Retail

Shelf Intelligence & Loss Prevention

In-store cameras infer stock levels and detect shrinkage in real time. Planogram compliance reports sync to HQ every hour.

Energy & Utilities

Grid Anomaly Detection

Substation edge nodes classify fault signatures locally, isolating grid segments before a cascade — milliseconds matter.

How It Works

Four phases from model deployment to cloud sync — all automated, all auditable.

01
Deploy

Package and push optimized models to edge hardware via our OTA pipeline — one click, any fleet size.

02
Infer

Models execute locally using hardware-accelerated runtimes. Zero cloud dependency at inference time.

03
Act

On-device logic triggers alerts, actuators, or downstream workflows in sub-10 ms — before the data leaves the room.

04
Sync

Aggregated insights, model gradients, and audit logs sync to the cloud when connectivity allows.

Powered By

Purpose-built products that make Edge Intelligence a production reality — not a prototype.

Inference Engine

EdgeCore Runtime

Lightweight ONNX and TFLite runtime optimized for ARM, x86, and RISC-V edge hardware with hardware-accelerated quantization.

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OTA & Orchestration

FleetSync

Manage model versions, configs, and firmware across heterogeneous device fleets with rollback and canary-deploy support.

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Computer Vision

EdgeVision

Pre-trained and fine-tunable vision models for detection, classification, and OCR — packaged for edge deployment out of the box.

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Intelligence Belongs at the Edge

See how duhitr.ai deploys production AI to your devices in days, not quarters. Talk to a solutions engineer.