Practical Edge AI Solutions — and How They Get Deployed
CCTV analytics, factory floors, retail stores and offline sites — practical edge AI that runs on hardware at your premises, deployed in five stages from pilot to rollout.
Existing cameras · new intelligenceTurn Existing Cameras Into AI Sensors
Business and operational analytics first — detections that improve safety, flow and operations, analysed on-site instead of in a remote cloud.
IP Cameras
LAN / PoE Switch
Edge AI Appliance
AI Analytics
Dashboard / Alerts
Video can be analysed on-site. Facial recognition and sensitive surveillance functions are not enabled by default — deployments focus on business and operational analytics.
Explore Computer VisionAI at the Factory Floor
Production environments can't wait on cloud round-trips — and often can't rely on uninterrupted connectivity. Industrial edge AI runs beside the line it monitors.
- Visual quality inspection
- Defect detection on production lines
- Equipment monitoring
- Predictive maintenance inputs
- Production-line analytics
- Safety monitoring
- Sensor analytics from existing PLC and IIoT data
Line-side inference
Multi-camera · on-siteRetail Intelligence That Stays In-Store
Understand footfall, queues and shelf activity across every camera — with footage processed on-site, store by store.
- Footfall analytics
- Queue analysis at checkout and counters
- Occupancy monitoring
- Shelf monitoring
- Customer-flow analytics
- Loss-prevention analytics
- Multi-camera processing per store or across sites
Keep Video and Sensor Data On-Site.
Inference on site hardware means footage and machine data are analysed where they are captured — and detection keeps running when the internet link drops.
- Raw streams stay on the site network; events travel only if you allow
- Local alerting and store-and-forward event logs
- Options for low-connectivity and air-gapped sites
On-site · inside your networkHow an Edge AI Deployment Is Planned
The same five stages apply to one store or a multi-site rollout. The order matters: workload first, pilot on real data, then scale.
Understand the Workload
Which decisions, from which cameras or machines, within what latency — and with what connectivity.
Select the Hardware
NPU, GPU or accelerator platform sized on stream count, models, power and environment.
Deploy the AI Stack
Linux, drivers, inference runtime, containers and the required models.
Integrate On-Site
Connect cameras, PLCs, alerting, dashboards and any business systems.
Run, Measure, Scale
Prove accuracy on real footage, then roll out site by site.
Pilot on real footage
Models behave differently on your cameras, lighting and angles than on any benchmark. A small pilot on a handful of streams shows real accuracy before a fleet-wide purchase.
Size with headroom
Measure throughput with your actual models and stream counts, then leave room for the next model and the next cameras. Vendor TOPS figures are a ceiling, not a sizing method.
Plan operations up front
Decide how models are updated, how devices are monitored and who receives alerts — before the first site goes live, not after.
Edge When It Matters. Cloud When It Helps.
You don't have to choose exclusively between cloud and edge. Keep sensitive, latency-critical workloads local — and use cloud selectively, where it genuinely helps.
Local Processing
Sensitive · real-time · offline-critical
Edge AI Platform
Owned by you · always available
Cloud Services
Optional · chosen per workload
Cloud shown optional — not a mandatory hop
Have an edge AI workload? Let's find the right hardware for it.
Tell us what you want AI to analyse on-site — cameras, machines, sensors or another workload — and we'll help map the infrastructure.