According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn documentation, Azure AI Custom Vision is specifically designed for training custom image classification and object detection models using images that a company provides. In this scenario, the company wants to identify competitor products from images captured in retail stores — a classic use case for custom image classification or object detection, depending on whether you are labeling entire images or identifying multiple items within an image.

Azure AI Custom Vision allows users to:

Upload their own labeled training images.

Train a model that learns to recognize specific objects (in this case, competitor products).

Evaluate, iterate, and deploy the model as an API endpoint for real-time inference.

This fits perfectly with the requirement that the solution “must be trained on images provided by your company.” The key phrase here indicates the need for a custom-trained model rather than a prebuilt one.

The other options are not suitable for this scenario:

B. Azure AI Computer Vision provides prebuilt models for general-purpose image understanding (e.g., detecting common objects, reading text, describing scenes). It is not intended for training on custom datasets.

C. Face service is limited to detecting and recognizing human faces; it cannot be trained to identify products.

D. Azure AI Document Intelligence (formerly Form Recognizer) is focused on extracting structured data from documents and forms, not analyzing retail images.

Therefore, per Microsoft’s official AI-900 training content, when a solution must be trained on custom company images to recognize specific products, the appropriate service is Azure AI Custom Vision.