Home/Technologies/Edge & Vision AI

Edge & Vision AI

Models that hold real-time latency inside a fanless box on your floor, your road or your gate.

Engineering depth

Intelligence at the edge.

Computer vision, connected devices and real-time intelligence for physical operations.

Designed for real enterprise requirementsDelivered with accountable engineering teamsSupported through deployment and operations
Edge & Vision AI at Purview
<100 msdecisions at the edge on fanless devices
FleetOTA updates across hundreds of endpoints in hours
Both sideswe tune the model and design the box it runs on
What Edge & Vision AI covers

Overview

Vision and edge AI is where applications meet physical infrastructure, and Purview works both sides: we quantise and tune models for edge silicon, design the devices they run on and deploy them at scale for safety, traffic, retail and industrial inspection.

Clients we serve

Who it is for

Plant and site safetyPedestrian and worker safety zones, PPE and intrusion detection.
Cities and operatorsANPR, traffic flow and crowd analytics.
ManufacturersInspection on the line and defect capture.
Retail and venuesFootfall, queue and dwell analytics.
Capabilities

What we deliver

  • Edge vision AI solutions
  • Edge SLM and on-device inference
  • AIoT pedestrian and worker safety
  • Footfall and crowd management
  • ANPR and traffic flow analytics
  • Industrial inspection and safety
  • Edge MLOps and model optimisation
Our approach

How we work

  1. Site surveyCameras, lighting, network and power assessed before any model work.
  2. Model to deviceQuantisation, operator fusion and NPU tuning against the real workload.
  3. Deploy fleetSigned OTA, device management and dashboards for hundreds of endpoints.
  4. ImproveEdge MLOps loop with labelled data flowing back to retrain.
Case in point

AIoT pedestrian safety

Industrial site, India. Vision models quantised for edge silicon, deployed on Purview-designed devices across vehicle zones with alerts to operators and a fleet dashboard.

Real-timeincident alerts, on-device
Results

Outcomes clients measure

Sub-100 ms decisions at the edge
Incidents detected before they become claims
Fleet-wide updates in hours, not weeks
Technology ecosystem

Platforms and partners

FAQ

Questions we are asked

Do you need cloud?

No. Inference runs on the device; the cloud is for fleet management and retraining.

Which silicon?

NVIDIA, DeepX and others, benchmarked on your model and power budget.

Privacy?

Analytics are computed on device; video need not leave the site.