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Edge AI Explained: Running AI Where the Data Is
What edge AI is, why businesses run AI models on cameras, gateways and devices, and how to design reliable edge AI systems for factories, retail and logistics.
Most AI runs in the cloud. Edge AI runs models directly on devices — cameras, gateways, industrial PCs, vehicles and phones — close to where data is produced.
Why run AI at the edge
- Speed: decisions in milliseconds, without a round trip to the cloud
- Bandwidth: analyse video locally and send only results, not hours of footage
- Privacy: sensitive images and data never leave the site
- Reliability: keeps working when connectivity drops
- Cost: lower cloud processing and data transfer bills
Where it is used
- Manufacturing: visual quality inspection and safety monitoring on the line
- Retail: footfall analytics, queue monitoring and shelf checks
- Logistics: parcel dimensioning, damage detection and vehicle monitoring
- Smart buildings: occupancy, energy and security
- Agriculture: crop and livestock monitoring
- Healthcare: monitoring devices that process signals locally
Hardware options
From small accelerator modules and AI-capable cameras to industrial PCs with GPUs. Choice depends on model size, number of streams, environment and budget.
Designing edge AI systems
- Optimise models for the target hardware with quantisation and pruning
- Manage devices centrally — deployment, monitoring and updates over the air
- Plan for failure — local buffering when connectivity drops
- Secure devices — secure boot, encrypted storage and network controls
- Collect feedback data to retrain models as conditions change
Edge and cloud together
The best systems split the work: edge for real-time decisions, cloud for training, dashboards, fleet management and heavy analysis.
See our computer vision and IoT development services.
