AI/ML Software Consulting and Development

Improve shopping experience and automate retailers’ efficiency

Product Recognition Solutions

Computer vision-based product recognition solutions are primarily employed in the retail sector, with a specific emphasis on customized solutions designed to detect and categorize items displayed on store shelves. These applications are commonly utilized for analyzing planograms and facilitating automated checkout processes.

Product recognition solutions

Planogram compliance

Utilizing cameras and mobile phones with computer vision-powered applications to analyze planograms and ensure adherence to the store’s strategy represents another move toward digitizing the retail environment.

Automated checkout

Automated computer vision-powered systems improves the shopping experience by reducing waiting times and enhancing convenience for customers, as well as decreasing costs on the retailers’ side.

Store stock audit

Store stock audits empowered by computer vision streamline and enhance the process of inventory management, providing real-time insights into stock levels and discrepancies.

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Key pre-requisites for implementing Product Recognition Solutions

Data quality

Just like with many other artificial intelligence and machine learning applications, the success of product recognition solutions is intricately linked to the quantity and quality of the data used during the model training phase.

Technology selection

When it comes to recognizing specific image classes, data quality, and various other factors, more technological options are at our disposal. Our responsibility lies in evaluating and choosing the right technology mix.

Data annotations

Properly annotated data, aligned with the expectations of the AI engineers responsible for training the model, can vastly improve the accuracy of the models.


Management of the expectations and proceeding step-by-step improves the overall satisfaction with the project development of implementation.

Key steps to your Computer-vision enablement journey

Usual lifecycle of computer vision-powered product recognition project

01 Challenges and goals

02 Understanding the data

03 Model experimentation

04 Benchmarks and targets

05 Evaluate, adjust, repeat

Recognizing your specific challenges and clearly outlining the goals we intend to achieve through our computer vision-powered solution is a vital and crucial first step in our collaboration.

Whether you possess in-house data or intend to utilize existing datasets, following an understanding of your business objectives, the first step involves data exploration and the thorough assessment of data quality, consistency and annotations.

Manual model training, conducted using the previously collected and refined data, yields certain outcomes. Following an evaluation of these results, we may need to iterate through this process several times before achieving the desired levels of accuracy, reliability, and solution stability.

Defining an acceptable level of accuracy, addressing edge cases, and determining the time and budget allocated for refining the model and data to attain optimal results are critical considerations. These aspects should be clearly outlined as benchmarks and metrics following their assessment in each iteration.

The evaluation of iterations, guided by established metrics, continues until the predetermined goals are met. Once these objectives are achieved, the solution is deemed ready for deployment and can be automated through an MLOps approach.

Discover why customers choose Altamira

Altamira produced superlative deliverables that provide valuable information to guide internal operations and support sales processes. The team was receptive to feedback, adapting resources to ensure effective collaboration.


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It has been an absolute pleasure working with the team at Altamira. We have never been blocked or impeded by their work. They operate efficiently and quickly to get the job done.


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The website that Altamira has generated now allows the company to save time and manpower as it performs a lot of tasks that other employees are used to doing. The team members' seamless approach to project management and their communication skills continue to make this collaboration productive.

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