https://store-images.s-microsoft.com/image/apps.60341.2db006c7-81a9-4815-b86b-b1cdef2ce553.ba8207d5-b801-42fb-bf14-97d084e1709d.ffbeea2a-0f5c-45cb-b99b-b7752f47efe0

SerendibAI

Zone TwentyFourSeven (Pvt) Ltd

SerendibAI

Zone TwentyFourSeven (Pvt) Ltd

An AI powered video analytics platform which generates insights from available video streams.

By integrating cutting edge Machine Learning ( ML ) with data science, the SerendibAI platform helps to convert real-world events into actionable insights. Through this platform, organizations are able to make data-driven decisions to optimize store environments to achieve business goals.

SerendibAI enables physical store insight generation at a level of detail that is equivalent to a web analytics platform generating analytics to an e-commerce portal. This allows organizations to optimize physical stores using data rather than intuition and measure success levels of initiatives in a manner that was not possible previously. The level of granular insights on customer segments and customer behavior is unparalleled.

  • Traffic Counting
    • Real-time traffic detection of people walking into a store or building.

  • Demographic Detection
    • Specialized algorithms are able to detect demographic details and segment people into customer segments such as Millennials Gen X, Gen Z, etc.

  • Fixture Dwell Time
    • Accurately measure customer time spent browsing at fixtures to give organization data on fixture engagement levels and measure the success of products on display.

  • Fixture Interaction
    • Identifies when customers stop and interact with products on fixtures.

  • Impression Count
    • Can accurately track gaze and identify impressions on a particular fixture which can be a retail fixture, display case or any general product display platform.

  • Line Monitoring
    • Tracking in-store lines and alerting staff members of particular changes to lines.
    • Continuously monitors all in-store lines and provides real-time alerts based on set variables such as line length etc.
    • Using data to predict wait time in order to provide useful information to the customers in real-time.

  • Line Prediction
    • The platform is able to predict how many checkout registers should be open at a given time frame to optimally cater to the checkout traffic.
    • The system analyzes Traffic counts, Occupancy levels, and Customer dwell times to forecast and provide guidance on the expected traffic in checkout areas.
    • The system can be used to effectively manage register operations to minimize line creation events and reduce line wait times.

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/images/videoOverlay.png
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