Ivy Eye – Old

Image Recognition Technology for Retail Execution

With high-speed processing capability and best-in-class accuracy, analyze store images in real-time.

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Automate Store Checks for a Perfect Store Execution

Our native image recognition solution, Ivy Eye, helps merchandisers capture images of shelves to analyze them and calculate KPIs in real-time for faster decision-making. With 97% accuracy and an image processing time of less than 3 min, your team delivers a perfect store execution.

  • Avoid manual data entry and calculate KPIs in real-time
  • Maximize your product visibility and augment sales
  • Minimize out-of-stocks and track availability
  • Planogram compliance and POSM tracking
  • Track prices of SKUs and spot variance
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Automate In-store Activities

  • Take photos of the shelves & automatically measure KPIs
  • Guided photo capture with angle and light intensity correction
  • Compare with perfect store KPIs and get actionable insights
  • Merchandisers complete tasks faster for better coverage

Intelligence that Impacts the Shelf

  • Calculate and track on-shelf metrics in real-time such as Share of Shelf, Availability, Share of Assortment, and Share of Display
  • Real-time alerts allow your team to get guidance
  • Take actions on compliance, pricing, and availability

Compliance Monitoring

  • Automatically scan the price of every SKU in the store and spot variance between actual vs. list price
  • Monitor promotional compliance and track POSMs store-wise for appropriate payouts
  • Easily compare the real planogram vs. the optimum planogram to gauge adherence

Faster Results

  • 97% Accuracy of Image Recognition Model and less than 3 min of image processing time
  • 35% of time savings versus manual data collection
  • Increased visibility and granular-level data tracking

Self-learning ML model

  • Images processed with our proprietary recognition engine and KPI’s are auto-calculated
  • Machine Learning model is set up for self-learning and auto correction
  • Results are pushed to the mobile app and backend portal within a few minutes

For All Shelf Conditions

  • Ivy Eye can easily distinguishing similar SKU’s from each other
  • Easily read small price tags using our advanced image recognition capability
  • Analyze image captured at any angle accurately
  • Handles low lighting shelf conditions

Enhanced Retail Execution

  • Ivy Eye is built-into our retail execution application, providing a unified experience
  • The solution pushes its results and actionable insights directly to the mobile app as well as back office
  • Enhanced capabilities to stay ahead of the competition

FAQs

Some of the questions we hear, we have tried to answer here. If you have some more questions,
please don’t hesitate to contact us. We are here to help you.

The initial training process may take about 3-5 weeks. During this timeframe, the AI model will reach an accuracy level of above 90%. That is when we start generating the KPIs, and subsequently, with another 2-3 weeks of training, it will reach close to 97% accuracy.

Ivy Eye accuracy level depends on factors of the input images like light conditions, camera clarity, focal distance, and image angle. We have seen slight modifications across different product categories and outlet conditions. We have never seen a fall below 95% in these cases.

Yes, the solution is compatible with both iOS and Android devices.

Yes, it is possible. We have standard APIs to transfer images from a third-party application to our image recognition engine and send the tracked KPIs back to their system.

We will have to do some incremental retraining of the model with new product images. The retraining can be done within a few days and takes only minimalistic efforts.

The process involves a workshop to understand the requirements, the system set-up, Machine training, and finally, roll out to the end users. The process typically takes about 8-12 weeks from start to finish the implementation.

No, image recognition can only process KPIs based on what is visible in an image. It can count the facings like the first row of the products on a shelf. The second and subsequent rows will not be visible on an image and hence will not be detected. But we have separate stock capture modules to capture the information manually.

Guided image capture is to address these challenges. We allow a 20% buffer space to overlap images captured so that merchandisers can effectively capture a sequential order of the aisles minimizing chances of errors and overlap of products.