HARVY vs TangoEye

HARVY is an AI compliance platform for Australian heavy vehicle operators. It automates NHVR work diary processing: you bulk-scan yellow work diary sheets, email them in, and HARVY extracts driver details, work and rest periods, and two-up information without manual data entry.

The platform checks each sheet against Standard Hours, Basic Fatigue Management, and Advanced Fatigue Management rule sets, including custom AFM rules. It flags breaches and missing data, lets you generate and track non-conformance reports, and stores sheets online for three years with quick search and export.

HARVY is built by Knkt Digital in Western Australia for compliance, operations, and HR teams at transport companies that need faster logbook review, audit readiness, and Chain of Responsibility visibility.

TangoEye is in-store retail analytics and automation software that turns existing CCTV cameras into footfall, zone, and conversion data. Instead of manual clickers or new sensor hardware, it plugs into the camera network retailers already run. The platform splits into four modules: Tango Traffic for walk-in counts, Tango Zone for shelf and area heatmaps, Tango RevOp for missed-sale tracking, and Tango StoreOps for camera and network uptime.

Most retail analytics vendors sell dedicated counting sensors or gate hardware. TangoEye's pitch is pure software on top of cameras that are already mounted, which keeps rollout cost lower and avoids ripping out existing infra. That trade-off means you need working CCTV coverage and bandwidth at each store, but you skip the cap-ex cycle of installing parallel sensor networks.

Retail operations teams use it to see bounced visitors, engaged shoppers versus casual walk-ins, and zone-level dwell time without standing at the door with a tally counter. Brand leaders at multi-store chains like Lenskart and Nykaa, listed on TangoEye's site, get a shared view of conversion leaks and infrastructure blind spots across locations. Store managers get hourly alerts when cameras or networks drop so footfall data does not silently go missing.