Great software, zero headaches
AI Product Recognition
For every business, improving customer experience and automating workflows remain ongoing priorities. That’s why, as your zero-headaches software development partner, we think ahead and fix problems before they hit your inbox. With product recognition solutions, you can find missing items, streamline stock management, and get more reliable data to draw your business processes upon without extra stress.
15+
Years
expertise
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Client
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Solve today’s challenges
Our clients
With Altamira, you get zero-headache software that keeps your shelves in order and store operations running smoothly.
As a rule, product recognition solutions are widely used in retail for two main purposes:
- You can check if products are arranged correctly on shelves
- You can recognise what customers are buying
Make it work for you
Planogram compliance
Computer vision-powered apps help you check if products are placed correctly on shelves, making it easier to follow the store’s plan and move toward a more digital retail setup.
Automated checkout
Computer vision systems make shopping faster and easier for customers while helping retailers cut costs.
Store stock audit
Computer vision makes stock audits faster and more accurate, giving you real-time updates on inventory and missing items.
Why Altamira?
Proactive problem-solving
We spot and solve issues before they impact you. Our streamlined development process ensures you get the most value from your investment.
Deep technical expertise
As a zero-headaches software partner, we have a proven track record of pushing the boundaries of what’s possible with technology.
Stress-free processes
We build trust through transparency in the way that we communicate and the continuous improvement in our processes and practices.
Case studies
People also asked
AI in computer vision refers to the implementation of artificial intelligence techniques to enable computers and systems to derive meaningful information from digital images, videos, and other visual inputs. It involves training machines to interpret and understand the visual world in a way that mimics human vision, using deep learning models and neural networks.
Yes, computer vision solutions utilise AI techniques, particularly machine learning algorithms, to interpret visual data. However, not all AI involves computer vision. AI can be applied in other domains like speech recognition, analytics, and language translation. Get in touch to discover more!
Yes, AI computer vision can predict outcomes based on visual data analysis. By training on large datasets, these systems can identify patterns and make predictions, such as forecasting trends, detecting potential defects in manufacturing, or anticipating movements in autonomous vehicles.
AI recognition refers to the ability of AI systems to identify and understand patterns, objects, speech, text, or data. Computer vision often involves recognizing and interpreting visual elements in images or videos, such as facial recognition, product recognition, object detection, and scene analysis. Contact us to learn how AI recognition can benefit your business.
AI can significantly facilitate product development by analysing market trends, customer preferences, and feedback. It can also streamline the design process through predictive modeling, automate testing and quality control with precision, and enhance customisation and personalisation of products based on consumer data analysis. For example, by integrating AI product recognition technology, retail stores can swiftly identify items for inventory management and enhance customer shopping experiences. Contact us to learn more!
Visual recognition AI is a technology that enables computers to identify and process visual data from the world around them. This includes identifying objects, faces, scenes, and activities in images and videos. It’s widely used in various applications, from security surveillance to healthcare diagnostics and consumer applications like photo tagging. For example, the latest advancements in computer vision recognition have significantly improved the accuracy of automated facial recognition systems used in security applications.
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