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GENERATION OF
OPERATIONAL IMPROVEMENTS

INTRODUCING A NEW ERA
OF DATA HARVESTING & ASSET TRACKING

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01

WHY DISRUPTIVE

Long story short: We transform STANDARD CAMERAS into high-precision sensors. Today, cameras capture millions of pixels in one single frame, providing nearly endless possibilities for data extraction. We can do it all: simple object recognition and visualizations - but also, and that’s the new and disruptive part, metric statutes, asset tracking/positioning, and even posture, and ergonomic analytics.

02

WHY COST-EFFICIENT

We can integrate existing cameras rather than required to install new ones. Thanks to local processing and in the cloud, we turn your cameras into an extremely cost-efficient sensor - with only one interface/API. Our methodology offers significant cost advantages, compared to current tracking solutions based on sender-receiver technologies like RFID or UWB.

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WE TRANSFORM
STANDARD
CAMERAS

... into high-precision sensors to support data-driven decision-making and improvement initiatives without the need for expensive hardware sensors.

*for Gamification, IIOT, Industry 4.0

03

WHY SUPERIOR

Superior is a big word, but we're proud to use it to describe our scientists from Silicon Valley to Scotland, who are clearly superior in computer vision, AI, and machine learning. The game changer? Leveraging this knowledge and developing an exclusive NEURAL NETWORK (International Patent) that enables the extraction of metric information (e.g. speed or distance) that surpasses all current computer vision capabilities.

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04

WHY ULTRA FAST [ROI]

We maintain projects in Australia and New Zealand, but without setting foot in those countries, all of our visualisations are done remotely. New methods of data visualisation (Digital Spaghetti Maps or Dwelling Heat Maps) allow us to develop solutions for improvements very quickly without performing extensive data analysis. Last but not least, our solutions show proven ROI with minimal cost/investment - for a couple of months or even just a few weeks.