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Service 03 · Edge Intelligence

Intelligence at the Edge

Deploy AI-driven workloads where the data originates — at the network edge. Eliminate cloud-round-trip latency, reduce bandwidth costs, and unlock real-time autonomous decision-making across your enterprise operations.

5G.agency architects Multi-access Edge Computing (MEC) platforms, AI/ML inference pipelines, and hybrid edge-cloud orchestration environments — all aligned to ETSI GS MEC standards and your 5G private network.

<10ms
Edge latency achieved
90%
Bandwidth reduction vs cloud
Real-time
AI inference at source
ETSI MEC
Standards compliant
The Problem with Cloud-First

Latency Kills Real-Time Operations

Sending data to a centralised cloud for processing introduces round-trip latencies of 50–200ms. For autonomous robotics, real-time quality control, and safety-critical systems, that gap is the difference between success and failure.

Edge Intelligence moves the compute to where the data originates — inside your private 5G network, millimetres from the sensor or machine — delivering sub-10ms response times that cloud architectures simply cannot match.

Public Cloud Processing 50–200ms
On-Premise Data Centre 10–50ms
5G MEC Edge Node <10ms

Edge Architecture Overview

Device & Sensor Layer
IIoT Sensors Cameras Robots AGVs
Private 5G SA Network (gNB/UPF)
uRLLC Slice eMBB Slice mMTC Slice
MEC Platform — Edge Intelligence Layer
AI/ML Inference · Real-time Analytics
Computer Vision Anomaly Detection Kubernetes
Cloud (Non-Latency-Critical Workloads Only)
90%+ of compute stays at the edge. Cloud is optional.
What We Deliver

Our Edge Intelligence Capabilities

A full-stack advisory and design service covering every layer of your edge intelligence architecture — from MEC platform selection to AI model optimisation.

MEC Platform Architecture

Design of ETSI GS MEC-compliant Multi-access Edge Computing platforms — selecting, sizing, and positioning edge nodes optimally within your private 5G topology for maximum performance and resilience.

  • ETSI MEC 003 / 010 / 011 compliance design
  • Hardware selection: GPU-accelerated edge servers
  • N6-LAN / UPF integration and traffic steering

AI/ML Model Optimisation

Specialised optimisation of AI and machine learning models for deployment on resource-constrained edge hardware — maintaining accuracy while reducing computational load by up to 80%.

  • Model quantisation, pruning & distillation
  • TensorRT, ONNX & OpenVINO edge deployment
  • Federated learning pipeline architecture

Real-Time Computer Vision

Deployment of computer vision inference workloads at the edge — processing live video streams from factory cameras and CCTV without transmitting raw video to the cloud, slashing bandwidth costs by up to 95%.

  • Defect detection & quality control at <5ms
  • Worker safety & PPE compliance monitoring
  • Object tracking for AGVs and robotics guidance

Edge-Cloud Orchestration

Hybrid orchestration architecture using Kubernetes and cloud-native tooling to manage workloads intelligently across edge nodes and cloud — ensuring latency-sensitive tasks run locally while batch workloads use cloud elasticity.

  • K3s / KubeEdge deployment & lifecycle management
  • GitOps-based workload deployment pipelines
  • Intelligent traffic offload policies & SLA guardrails

Anomaly Detection & Predictive Maintenance

Edge-deployed ML models that continuously analyse sensor data streams to detect equipment anomalies, predict failure events, and trigger automated maintenance workflows — before downtime occurs.

  • Vibration, temperature & acoustic anomaly models
  • Predictive failure alerting with 94%+ accuracy
  • CMMS integration for automated work-order creation

Edge Security Architecture

Zero-Trust security design for edge environments — ensuring every workload, API endpoint, and data flow at the edge is authenticated, encrypted, and auditable, regardless of physical location.

  • mTLS between all edge microservices
  • Hardware attestation & secure enclave design
  • NIST SP 800-207 Zero Trust alignment
Industry Applications

Edge Intelligence in the Real World

Concrete use cases where edge AI on private 5G delivers measurable business outcomes — not theoretical frameworks.

Manufacturing

Automotive · Electronics · Pharma

Visual Quality Inspection

Computer vision models running on MEC nodes inspect 100% of production output in real-time — reducing defect escape rates by up to 94% vs. manual sampling.

Collaborative Robot Control

Sub-1ms control loops for cobot arms eliminate safety stops and enable human-robot collaboration on shared production lines at full operational speed.

Predictive Maintenance

Edge ML models on vibration and acoustic sensors predict CNC machine failures 72 hours in advance — reducing unplanned downtime by an average of 67%.

94%
Defect detection rate
-67%
Unplanned downtime
<1ms
Robot control loop

Logistics & Ports

Warehouses · Container Terminals

AGV Fleet Intelligence

Edge AI coordinates hundreds of autonomous guided vehicles in real time — routing, collision avoidance, and priority management without cloud dependency.

Automated Inventory Vision

Edge cameras with OCR and object detection models provide real-time inventory accuracy across 1M+ SKUs — eliminating manual cycle counts entirely.

Sub-10cm Asset Positioning

MEC-powered 5G positioning provides real-time location of every pallet, container, and vehicle — enabling zero-dwell-time operations at scale.

+47%
Throughput uplift
-22%
Dwell time
99.9%
Inventory accuracy

Healthcare

Hospitals · Clinical Networks

Real-Time Patient Monitoring

Edge AI processes continuous biometric streams from 10,000+ IoMT devices locally — detecting deterioration events in <500ms with clinical-grade data sovereignty.

AR-Assisted Surgery

MEC-hosted surgical AR rendering delivers holographic overlays with <8ms motion-to-photon latency — enabling precise, real-time guidance for surgeons at the point of care.

Clinical Data Sovereignty

Patient data never leaves the hospital premises — edge processing ensures GDPR, HIPAA, and NHS DSPT compliance by design, with no cloud exposure risk.

<500ms
Alert latency
-62%
Critical incidents
100%
On-premise data
Technology Stack

Standards & Platforms We Work With

Vendor-neutral advisory across the full edge intelligence technology ecosystem.

Kubernetes / K3s
KubeEdge
NVIDIA TensorRT
OpenVINO
ETSI MEC
Zero Trust / mTLS
Edge Intelligence FAQs

Common Questions

Everything enterprise technology leaders ask us about MEC and edge AI deployment.

AI powers big data analysis and automation workflows
Start Your Edge Journey

Move Intelligence to Where It Matters

Start with a 5G Readiness Audit that includes a full edge intelligence capability assessment — and receive a concrete blueprint for deploying AI at the edge of your private 5G network.

MEC platform recommendation tailored to your workloads
AI/ML use-case prioritisation and ROI modelling
Edge-cloud architecture blueprint included
Vendor-neutral hardware and software recommendations

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