Deploy AI inference on-device and at the edge for low-latency, offline-capable intelligent systems.

Edge AI

A specialized capability within DEQX AI Transformation — run inference closer to where data is generated so teams decide in milliseconds, operate offline, protect sensitive data, and scale intelligence across factories, stores, vehicles, and field devices.

Low-Latency
Decisions at the device or plant edge
Offline-Ready
Inference without constant cloud connectivity
Hardware-Fit
Models optimized for constrained silicon
Fleet OTA
Remote updates across distributed edge nodes

Edge Intelligence, Industrialized.

From on-device inference and model compression to vision at the edge and fleet operations — one architecture for real-time, privacy-aware, production-grade Edge AI.

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On-Device & Edge Inference

Run models on industrial PCs, gateways, GPUs, NPUs, and embedded devices so latency-critical decisions never wait on a round trip to the cloud.

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Privacy at the Source

Keep raw sensor streams and proprietary footage local. Ship only insights, events, and aggregates — reducing exposure and supporting data residency requirements.

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Real-Time Vision, Sensing & Fleet Scale

Combine computer vision and sensor fusion with offline sync, remote model OTA, and observability so edge deployments stay reliable from pilot rack to multi-site fleet.

Explore Integrations arrow_forward

What We Deliver

Structured Edge AI capabilities that map from constrained hardware to governed fleet operations — designed for industrial, retail, logistics, and field environments.

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On-Device Inference Runtimes

  • check_circlePackaged runtimes for IPC, gateway, and appliance targets
  • check_circleGPU, NPU, and CPU serving profiles by workload
  • check_circleLocal APIs and event buses for downstream automation
speed

Model Optimization for Edge

  • check_circleQuantization, pruning, and distillation for target silicon
  • check_circleLatency, power, and accuracy trade-off modeling
  • check_circleHardware-aware benchmarks before go-live
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Offline & Limited Connectivity

  • check_circleFully local inference when networks drop or are denied
  • check_circleBuffered events with conflict-aware cloud sync
  • check_circleGraceful degradation and store-and-forward patterns
visibility

Edge Computer Vision

  • check_circleQuality inspection, safety, and anomaly detection
  • check_circleVideo analytics with spatial and temporal intelligence
  • check_circleCamera-to-decision pipelines under strict latency SLOs
cloud_sync

Fleet Management & OTA

  • check_circleModel and runtime OTA with staged rollouts
  • check_circleDevice inventory, health, and version inventory
  • check_circleCanary nodes and instant rollback across sites
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Privacy & Security at the Edge

  • check_circleLocal processing to minimize sensitive data egress
  • check_circleSecure boot, signed artifacts, and access controls
  • check_circleAudit trails for inference events and model versions
hub

Industrial Systems Integration

  • check_circleMES, WMS, ERP, and SCADA event wiring
  • check_circleIoT brokers, OPC-UA, MQTT, and custom protocols
  • check_circleOperator dashboards and automation handoffs
cloud

Hybrid Edge–Cloud Architecture

  • check_circleDecide what stays local versus what escalates to cloud AI
  • check_circleCentral training with distributed edge serving
  • check_circleUnified observability across edge and cloud tiers
developer_boardEdge Runtime

Inference where the work happens.

Milliseconds matter on the line, at the dock, and in the field. We package models and runtimes for industrial PCs, gateways, and GPU appliances so decisions land where operators and machines already act — without depending on cloud round trips.

  • memory

    Target Hardware Fit

    Select and validate silicon profiles — CPU, GPU, NPU, or accelerator — against real latency, thermal, and power envelopes.

  • deployed_code

    Production Packaging

    Containerized or embedded runtimes with health checks, local queues, and clear interfaces for plant and OT systems.

  • system_update

    Remote Lifecycle

    Signed model and config updates with staged promotion so fleets stay current without truck rolls or downtime theater.

videocamVision & Sensing at the Edge

See, detect, and decide in real time.

Combine edge inference with computer vision and sensor fusion to inspect, detect, and respond on the factory floor, in retail, or across logistics — with privacy controls that keep raw media local when required.

  • speed

    Low-Latency Vision Pipelines

    Optimized detection and classification for quality, safety, throughput, and exception handling under strict SLOs.

  • sensors

    Sensor Fusion

    Blend cameras, PLC signals, IoT telemetry, and environmental sensors into coherent local decisions.

  • visibility_off

    Privacy-Preserving Capture

    Process frames on-device; emit events and metadata instead of streaming every pixel to the cloud.

hubSystems Integration

Wire edge intelligence into operations.

Insights only create value when they reach MES, WMS, ERP, and operator workflows. We integrate edge events into the systems your teams already trust — so AI closes the loop with automation and human oversight.

  • lan

    OT & IT Bridges

    Connect gateways and inference nodes to industrial protocols and enterprise APIs with durable messaging.

  • engineering

    Operator & Automation Handoffs

    Surface alerts, work orders, and automated actions with clear ownership and escalation paths.

  • account_tree

    Hybrid Escalation

    Keep hot-path inference local; escalate complex cases to private cloud or VPC models when bandwidth and policy allow.

tuneOperating Modes

Built for Constrained Reality

Enterprise Edge AI must survive bandwidth limits, intermittent connectivity, and hardware budgets — not just demo networks.

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Offline-Capable Inference

Continue detecting, classifying, and deciding when WAN links fail — with buffered sync when connectivity returns.

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Bandwidth-Aware Design

Transmit compact events and summaries instead of raw video, reducing cost and protecting sensitive streams.

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Power & Thermal Budgets

Tune model size and batching to fit embedded and industrial enclosures without thermal throttling.

Edge Deployment Patterns

Proven patterns for how enterprises place inference relative to cameras, PLCs, and cloud control planes — aligned with DEQX AI Transformation programs.

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Device Edge

Models on cameras, handhelds, or embedded boards for ultra-local decisions.

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Plant / Store Edge

Shared GPU or NPU appliances serving multiple lines, bays, or floors.

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Regional Gateway

Aggregated inference and sync hubs for multi-site fleets with limited uplink.

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Hybrid Cloud Assist

Edge for hot path; private cloud for heavy models, training, and analytics.

Hardware, Runtime & Operations Stack

Cloud-agnostic edge delivery across industrial hardware, inference runtimes, and enterprise systems — the same integration discipline that powers DEQX AI Transformation and computer vision programs.

memoryNVIDIA / GPUs
developer_boardIndustrial PCs
routerIoT Gateways
boltONNX / TensorRT
sensorsMQTT / OPC-UA
account_treeMES / WMS / ERP
security

Secure Artifact Pipeline

Signed models, config packages, and runtime updates with environment promotion from lab hardware to production fleets.

monitoring

Observability Everywhere

Latency, accuracy proxies, device health, and sync status — visible centrally without shipping raw edge media off-site.

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Works With Your Edge & Enterprise Stack

Deploy and operate Edge AI beside the industrial, cloud, and data platforms enterprises already run.

memoryNVIDIA
developer_boardIntel / AMD
settingsONNX Runtime
boltTensorRT
deployed_codeKubernetes
cloudAWS / Azure / GCP
sensorsMQTT Brokers
dnsOn-Premise OT

How We Deliver Edge AI Programs

A disciplined path from use-case and hardware discovery to industrialized fleets — designed to avoid lab demos that never survive the plant floor.

Phase 01

Discover

Map latency, privacy, connectivity, and hardware constraints. Prioritize use cases where on-device or plant-edge inference creates measurable operational lift.

Phase 02

Pilot on Target Hardware

Optimize and package models for real devices. Validate accuracy, latency, thermal limits, and offline behavior in a controlled production-like environment.

Phase 03

Industrialize

Harden OTA, monitoring, integrations to MES/WMS/IoT, and security baselines. Expand from a single line or site to a governed multi-node footprint.

Phase 04

Operate & Scale

Fleet health, model drift management, staged rollouts, and continuous improvement as cameras, lines, and products evolve.

Frequently Asked Questions

Model optimization for target hardware, packaging and update mechanisms, local inference runtimes, offline/sync patterns, fleet observability, and integration with upstream systems so insights reach operators and automation layers in real time.

Ready to run AI at the edge?

Let's Talk