Posts by Caglar Aybas

About Caglar Aybas

Caglar Aybas founded IoTiq in Halifax after studying Industrial Automation Technologies, with a background in mechanical and electrical engineering. That hands-on foundation started early — competing in line-following robotics competitions — and now shows up in how IoTiq systems get specced, wired and tested before they ship. Every guide on this blog is written from that same practical background: real wiring diagrams, real compatibility checks, and real Canadian climate conditions, not manufacturer marketing copy. Connect on LinkedIn.

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AI-based workplace safety monitoring for helmets, hi-vis vests, and PPE violations in real time, with industrial compliance data and a warehouse safety scene.

12 Sep

IoTiq Blog

AI for Workplace Safety: Detecting Helmets, Vests and PPE Violations in Real Time

Posted by

Caglar Aybas

September 4, 2026

PPE compliance is near-perfect during audits and drifts the rest of the time. AI detection provides continuous coverage between inspections — but only if the alert design survives contact with a real site.

A license plate recognition system captures vehicle plates at a gated entrance to automate access control, parking enforcement, and logistics tracking.

09 Sep

IoTiq Blog

Licence Plate Recognition Systems: How AI Automates Access Control, Parking and Logistics

Posted by

Caglar Aybas

September 4, 2026

Licence plate recognition reads a plate, checks it against a list, and opens a gate or writes a log line. Here is how the pipeline actually works, where it pays for itself, and why placement matters more than the model.

AI video analytics turning existing CCTV cameras into real-time business intelligence for commercial buildings, warehouses, retail sites, and multi-location operations in Canada.

06 Sep

IoTiq Blog

How AI Video Analytics Turns Existing CCTV Into Real-Time Business Intelligence

Posted by

Caglar Aybas

September 4, 2026

Most commercial buildings already have cameras, but nobody is watching them. AI video analytics closes the gap between recorded footage and understood events — turning passive CCTV into real-time alerts and operational data.