A Google Cloud study revealed something few expected: despite all the talk about artificial intelligence in digital retail, the use of multimodal AI in Brazilian e-commerce is still quite limited. Most operations still treat text, image, audio, and video as separate data, and that's precisely where the bottleneck lies.
Multimodal AI breaks this logic, as it processes multiple types of data simultaneously, combining language, image, audio, and video into a single model. For e-commerce, this represents a shift in how consumers will interact with online stores in the coming years.
Multimodal AI: What Really Matters for Digital Retail
Before we begin, it's worth understanding a little more about the concept and what it means in the context of those who operate or decide on an online store.
- Multimodal AI is an artificial intelligence model capable of understanding and generating content in different formats simultaneously. (text, image, audio and video). Unlike unimodal models, She doesn't need separate pipelines for each type of data.;
- Visual search is one of the most immediate applications in e-commerce. The consumer photographs a product they saw on the street and finds the equivalent item in the store, without needing to describe in words what they want;
- Multimodal chatbots go beyond text. They interpret photos sent by the client (“Does this product go well with this?They respond by voice and understand short videos shared in the conversation;
- Product content generation becomes scalable. AI reads catalog images and generates accurate descriptions, SEO tags, and copy variations, without manual intervention for each SKU;
- In the Brazilian context, adoption is still in its early stages. According to the Google Cloud study cited by Retail Center, Most e-commerce operations in Brazil have not yet integrated multimodal AI in a structured way.
- The most relevant models on the market today (such as OpenAI's GPT-4o, Google's Gemini, and Anthropic's Claude) They are already multimodal by default. The barrier is no longer one of technology, but rather one of implementation.
Why Multimodal AI Matters
E-commerce has faced a consistent increase in consumer expectations in recent years. Speed, personalization, and ease of search are no longer differentiators; they've become requirements. And much of this pressure stems from a simple change: people no longer search using only keywords.
According to Google data, billions of visual searches are performed monthly using Google Lens. This number is growing especially in the fashion, home decor, and electronics retail sectors—categories where visual appeal is the primary trigger for purchase. An online store that relies solely on text-based searches is losing a growing share of purchase intent.
Multimodal AI responds to this change in behavior because it processes data in the format in which it arrives, without forced conversion.
HOW DOES THIS TRANSLATE TO REAL-WORLD E-COMMERCE OPERATIONS?
Image search and product discovery
A consumer, for example, sees a pair of sneakers in an Instagram post and wants to buy them. With integrated visual search (made possible by multimodal AI), they upload the image to the store and receive relevant results immediately . Without needing to describe color, model, style, or brand.
Platforms like VTEX and Shopify already offer integrations with computer vision APIs. The competitive advantage, however, lies in how this layer is trained with the specific catalog of the operation.
Customer service that understands visual context.
Chatbots based on multimodal models can interpret images sent by the customer during a conversation. If the user sends a photo of a defective product, the model identifies the problem, consults the order history, and directs the solution, all within the same flow.
This reduces the average resolution time and decreases the need to escalate to human assistance in simple cases.
Generating catalog content at scale.
Operations with thousands of SKUs have a chronic problem: describing each product individually is time-consuming and leads to inconsistency . Multimodal models read the product image, identify visual attributes (color, texture, shape, composition), and automatically generate descriptions, feature bullets , and category tags.
According to IBM, this type of automation has a direct impact on the speed of new product launches and the quality of on-page catalog SEO.
Personalizing the experience through visual behavior.
In addition to processing images submitted by the user, multimodal AI can analyze the visual pattern of products that the consumer clicked on, added to their cart, or purchased . This allows the recommendation engine to work with visual attributes , not just manually entered categories and tags.
The result is a thinner layer of customization, especially in categories where style is subjective.
What the Brazilian market still needs to resolve.
The Google Cloud study is clear: the adoption of multimodal AI in Brazilian e-commerce is still limited . The reasons vary, but some patterns frequently appear in medium and large-scale operations:
Fragmented data. Catalog images without standardization, videos lacking metadata, and inconsistently generated text hinder the training and integration of multimodal models.
Lack of clarity regarding priority use cases. Many teams discuss technology in the abstract, without mapping where it solves a real conversion, operational, or customer service problem.
Dependence on custom integrations. The APIs for the major models (Google Gemini, OpenAI GPT-4, Anthropic Claude) exist and are mature. The bottleneck is usually in the integration with legacy e-commerce platforms and in internal content approval workflows.
These are not insurmountable obstacles; they are design and prioritization problems, not technological ones.
Metrics that change with multimodal AI
Finally, implementing multimodal AI in an e-commerce operation has a measurable effect on specific indicators. The most relevant to monitor are:
- Conversion rate in internal search: Visual search reduces friction between intent and relevant results;
- Rate of cart abandonment: Multimodal customer service resolves product queries more accurately, reducing dropouts due to uncertainty;
- Catalog production time: Automated description generation reduces the time between receiving the product and publishing it in the store;
- Return rate due to product dissatisfaction: When descriptions and images are generated consistently, customer expectations align better with the product received.
The topic of multimodal AI is still under construction in the Brazilian market, which means there's real room for those who position themselves now. On the e-Plus blog , you'll find other content about artificial intelligence, e-commerce, and technology for digital retail. Explore the articles and stay informed about what's changing.
FAQ – Frequently Asked Questions about Multimodal AI
What is multimodal AI?
Multimodal AI is a type of artificial intelligence capable of processing and generating content in multiple formats simultaneously (text, image, audio, and video). Unlike traditional models, it does not need to convert one type of data into another to process it, which makes interactions more natural and results more accurate.
How can multimodal AI increase e-commerce sales?
Multimodal AI directly improves product search, customer service, and catalog quality. Visual search makes it easier for consumers who can't describe what they want in words to discover products. Multimodal chatbots resolve queries with more context. Together, these factors reduce friction and increase conversion.
Which e-commerce platforms already support multimodal AI?
VTEX and Shopify already offer integrations with computer vision APIs and multimodal language models. The level of support varies by platform and use case. Integration with models such as GPT-4, Gemini, and Claude is possible via API in virtually any technology stack.
Is Brazilian e-commerce ready to adopt multimodal AI?
According to research by Google Cloud, the use of multimodal AI in Brazilian e-commerce is still limited. The main obstacles are fragmented data, lack of catalog standardization, and the absence of well-defined use cases, not technological limitations. Operations that address these points will have a competitive advantage in the coming years.
