AI in VTEX: How the Platform Is Transforming E-Commerce in Retail and FMCG 

VTEX is an enterprise e-commerce platform that brings B2C, B2B, marketplace, and omnichannel sales together. Its built-in AI capabilities support data analysis, product search, personalization, content creation, and customer service. 

AI is built into the VTEX commerce environment rather than added as a separate tool. AI agents can use data from the product catalog, orders, inventory, promotions, and customer behavior to analyze situations and carry out specific actions. 

It is also worth noting that the AI capabilities built into the VTEX CX Platform support two main types of users: 

• strategic and operational teams, including e-commerce directors, managers, and specialists responsible for campaigns and product catalog management, 

• consumers visiting an online store to find products, information, or inspiration. 

This is particularly important in retail and FMCG. Large catalogs, frequent campaigns, pressure on margins, seasonality, and thousands of daily customer interactions make it increasingly difficult to manage every aspect of sales manually. 

Key Takeaways:

VTEX is developing AI capabilities across several connected areas: 

• data analysis and automation for e-commerce teams, 

• product search and personalized recommendations, 

• content creation and optimization, 

• conversational commerce and post-purchase service, 

• retail media campaigns, 

• platform development and integration by technology teams. 

The greatest value does not come from the number of individual tools. What matters most is their access to shared business context and their ability to connect analysis, decision-making, and execution within a single platform.

What Is VTEX? 

VTEX brings the key components of digital commerce into one environment, including the product catalog, CMS, checkout, order management, marketplace capabilities, and integrations with external systems. It enables businesses to expand across channels and markets without having to build a separate solution for every process. Its shared architecture and access to data also provide a foundation for using AI in the day-to-day work of e-commerce teams.

VTEX is used by global brands including OBI, InPost Fresh, Carrefour, The North Face, Unilever, C&A, Decathlon, Whirlpool, KFC, H&M, Philips, Nestlé, 7-Eleven, Adidas, and Mondelez. These organizations operate across multiple markets and sales channels while maintaining a strong focus on operational efficiency and margins.

A Library of Ready-to-Use AI Agents 

VTEX provides a library of ready-to-use AI agents that businesses can deploy without having to build their own artificial intelligence models. Each agent supports a specific business process and can assist both online shoppers and teams responsible for sales, marketing, and e-commerce operations.

Key capabilities include:

• Ready-to-deploy AI use cases: businesses do not need to develop every solution from scratch.
• Support throughout the sales process: agents can assist with product discovery, customer service, merchandising, and catalog management.
• Automation of repetitive tasks: this reduces the workload of e-commerce teams and speeds up everyday activities.
• Use of company data: agents work with information available within the platform and follow predefined business rules.
• Control over AI activities: organizations can define permissions, operating rules, and the level of autonomy granted to each agent.
• Gradual implementation: individual agents can be introduced first, with AI adoption expanded as the organization develops.
• Integration with the VTEX ecosystem: agents work with platform modules to support consistent omnichannel and marketplace processes.

Below, we look at selected AI agents available in VTEX and examples of how they can support the day-to-day work of an e-commerce organization.

Data Insights Agent: From Reporting to Action 

E-commerce teams have access to more data than ever, but simply collecting it does not solve business problems. Teams still need to know what to look for, how to connect information from different areas, and which findings should lead to action.

The Data Insights Agent available in VTEX allows users to ask questions about sales performance in natural language. Instead of building another report manually, a manager might ask:

• Which products experienced a decline in conversion last week and, more importantly, why did it happen?
• Which categories generate high traffic but relatively few sales?
• Which orders fail to reach the expected margin?
• Show me the products where we make less than X.
• In which areas have unusual changes in performance occurred?

The agent can help uncover anomalies, trends, and potential causes of a problem. A drop in sales does not always mean that customers have lost interest in a product. Low inventory, incorrect pricing, an incomplete description, an unavailable variant, or a confusing product detail page may cause it.

How Does VTEX AI Workspace Coordinate AI Agents? 

VTEX AI Workspace represents the next stage of this development. It is an environment designed to coordinate the work of specialized agents. One agent may identify a problem, another may analyze possible solutions, while a third supports the implementation of changes in search, the product catalog, content, or pricing.

“The biggest change is that AI does not stop once it has prepared a report. It can identify the cause of a problem, recommend the next step, and, in selected processes, help carry it out. The team still controls the platform’s priorities and operating rules, but an agent can also work autonomously within predefined boundaries.

A good example is optimizing the conversion rate of product detail pages. An e-commerce director could simply ask an AI agent to:

1. Find products with high traffic but low conversion

For example, show me all products receiving more than 500 visits per month but achieving a conversion rate significantly below the average for their category. The AI agent can prepare the list, saving the team many hours of manual analysis.

2. Identify the most likely causes of low conversion

The AI agent analyzes the data and develops a list of hypotheses, such as:

• the product has too few images,
• the description is too short or lacks important information,
• customers cannot choose between relevant variants, such as size or color,
• the price is higher than that of comparable competing products,
• the product has poor reviews or no reviews at all,
• the product detail page is missing important technical specifications.

3. Implement the recommended changes

The e-commerce director can approve selected recommendations and pass them to other AI agents. These agents can prepare new descriptions, generate additional content, fill in missing information, or suggest merchandising changes. Wherever human judgment is required, the agent presents its recommendations for approval.

4. Review the results and learn from them

After two or three weeks, the e-commerce director can ask the AI agent to analyze the results. The system identifies which changes improved conversion, which failed to deliver the expected outcome, and what the next optimization cycle should focus on.

This shows that AI is becoming more than an analytics tool. It can act as a genuine partner for the e-commerce team. In large organizations managing thousands of products, this way of working can save hundreds of hours each month, speed up implementation, and enable teams to make better use of their expertise,” says Tomasz Cyrek, Country Manager at VTEX.

AI Can Help Protect Margins, Not Just Increase Sales 

In retail and FMCG, strong sales do not always translate into strong profitability. A small order may appear profitable at the product margin level but become unprofitable once picking, packaging, payment, and delivery costs are included.

A properly configured agent can monitor such cases and identify orders that fail to meet a predefined minimum absolute margin. It may then recommend actions such as:

• changing the minimum order value,
• applying more relevant cross-selling,
• promoting higher-margin products,
• adjusting free shipping rules,
• changing the visibility of selected products.

This does not need to be a one-time analysis. The agent can monitor performance continuously and flag the issue whenever it appears again.

This requires the right data model, a clear understanding of costs, and rules tailored to the specific business. Not every organization will need the same use case. This is why agent design should begin with a business process and objective rather than with the choice of an AI model.

A Search Engine That Better Understands Customer Intent 

Customers do not always use the same product names as those found in the catalog. They may use informal terms, enter incomplete phrases, or describe a need instead of naming a specific product.

In FMCG, searches may include phrases such as:

• sugar-free snacks,
• food for a barbecue with a few people,
• quick dinner ideas for kids,
• skincare for sensitive skin,
• limescale remover.

A traditional search engine attempts to match the words entered by the customer with product names and attributes. AI Search & Recommendations is intended to consider context, user intent, and seasonality, such as summer, winter, or the holiday season, and then combine search results with personalized recommendations.

For the retailer, this can shorten the path to the right product and reduce the number of searches that return no results. For the customer, it creates a more natural way to navigate a large product assortment.

The system can also consider seasonality, previous behavior, product availability, and merchandising rules. At the same time, the platform operator can continue to define priorities, weights, and rules governing how products are displayed.

AI does not need to take complete control of search results. It can automate selected decisions within boundaries established by retailers, merchandisers, and e-commerce teams.

A Virtual Advisor Available Throughout the Customer Journey 

A traditional chatbot often ends a conversation by providing a link to a help article or transferring the customer to a service representative. An AI agent integrated with a commerce platform can go further.

The VTEX CX Platform gives agents access to data from the product catalog, order management system, promotions, and checkout. As a result, an agent can not only answer a question but also complete a specific task.

Depending on how the solution is configured, a customer may be able to:

• find a product that meets specific needs,
• compare available variants,
• check the status of an order,
• start a return or exchange,
• cancel an order,
• recover an abandoned shopping cart,
• complete a purchase through a conversational channel.

In retail and FMCG, such an agent may answer questions about ingredients, instructions for use, availability, product variants, or current promotions. This depends, of course, on access to structured and up-to-date product data.

When human judgment is required, the conversation can be transferred to a customer service representative together with the existing context. The customer does not need to explain the entire situation again.

Generating Content at Scale for Large Catalogs 

Retailers and FMCG manufacturers regularly prepare product descriptions, category pages, seasonal campaigns, promotional messages, and content versions for different markets.

With thousands of products, even a small update can require many hours of work. CMS with GenAI is intended to support content generation, adaptation, and optimization while maintaining approval processes and control over published changes.

It may support activities such as:

• preparing content variations for different customer groups,
• localizing campaigns for new markets,
• expanding category and product descriptions,
• adapting communication to a particular season or channel,
• creating landing pages more quickly,
• optimizing content for search engines.

The goal is not to automatically publish everything generated by the model. The more valuable outcome is reducing repetitive work while maintaining brand consistency, data accuracy, and editorial control.

Retail Media as an Additional Revenue Stream 

Retail media is particularly important to retailers and FMCG brands. Retailers have access to traffic, advertising inventory, and data about customer behavior. Manufacturers, meanwhile, want to reach shoppers at the point when they are making a purchase decision.

VTEX Ads Platform enables campaigns that include sponsored products, advertising placements within the online store, activities outside the storefront, and advertising formats used in physical stores.

In practice, a manufacturer of beverages, cosmetics, or food products can promote its offering directly in search results or on category pages. The retailer gains an additional revenue stream, while the advertiser reaches audiences who are closer to making a purchase than users of many traditional advertising channels.

The AI layer is intended to support areas including:

• creating campaigns based on objectives described in natural language,
• selecting target audiences,
• allocating budgets,
• monitoring performance,
• recommending changes while a campaign is running.

Some agent-based campaign management capabilities are still on the product roadmap. VTEX Ads already supports on-site, off-site, and in-store activities.

AI Also Supports Technology Teams 

VTEX also uses AI to support technology teams. MCP Server and a set of 42 AI Skills are intended to help developers understand the platform architecture and build integrations and extensions faster.

The offering includes VTEX Developer MCP and the VTEX Skills catalog. MCP gives coding assistants access to up-to-date documentation and API specifications. Skills provide context on architecture, platform limitations, security, and recommended implementation patterns.

The catalog includes 42 skills covering areas such as FastStore, VTEX IO, marketplaces, payments, commerce architecture, and headless solutions.

This can streamline the development of integrations with payment systems, marketplaces, front-end solutions, and external services. AI can help developers find the right endpoint more quickly, account for platform constraints, and prepare an initial version of the solution.

It does not remove the need for code reviews, testing, security controls, or an assessment of whether the solution fits the client’s architecture.

AI Does Not Mean Giving Up Control 

One of the most common concerns surrounding AI is the potential loss of control over decisions made by the system. This is particularly relevant to personalization, pricing, promotions, and product presentation.

In an agent-based model, the team’s role gradually shifts from performing every task manually to defining objectives, limitations, and operating rules. The platform operator can still decide:

• which data may be used,
• which actions require approval,
• which products and campaigns should be prioritized,
• when an agent should transfer a task to a person,
• how the effectiveness of its actions should be measured.

In large organizations, this control should be supported by permissions, change histories, approval processes, and regular performance monitoring.

AI can operate quickly and analyze far more options than a person. It does not, however, replace the organization’s business or technological responsibility.

Why Does This Approach Work for Retail and FMCG? 

Retail and FMCG are industries in which even a small improvement to a repetitive process can have a significant impact on results. This is due to the scale of operations and the frequency of customer interactions.

AI can provide particular value when organizations have:

• large and frequently changing product catalogs,
• a high volume of promotions and campaigns,
• seasonal changes in demand,
• low margins per product,
• multiple sales and order fulfillment channels,
• a large number of order-related inquiries,
• a need to monetize traffic through retail media,
• a need to localize content quickly.

VTEX brings the commerce platform, AI agents, and retail media together within one system. This allows AI to support more than a single stage of the process. It can play a role throughout the journey, from product discovery and purchase to order service and follow-up customer communication.

How Should Businesses Approach AI Implementation in VTEX? 

The first step should not be to launch every available agent. A better approach is to select a process that creates a visible cost, limits sales, or requires a significant amount of manual work.

Examples include:

• analyzing high-traffic products with low conversion rates,
• handling order status inquiries,
• improving search quality,
• automating content localization,
• recovering abandoned shopping carts,
• improving order profitability,
• launching initial retail media campaigns.

The next step is to define a measurable objective, assess data quality, and determine how much autonomy the agent should have. Once the results have been verified, the solution can be expanded to additional processes and channels.

Univio supports companies in analyzing their architecture, designing processes, and implementing and developing the VTEX platform. We help organizations select solutions suited to their business model, integrate the platform with their existing technology environment, and prepare for the secure use of AI agents.

Would you like to find out which VTEX capabilities could deliver the greatest value to your organization? Contact the Univio experts to discuss a potential implementation approach.

FAQ 

Are All of VTEX’s AI Features Already Available? 

No. Some capabilities are available through Priority Access, while others are still on the product roadmap. Availability may depend on the market, account, and selected product. Before starting a project, it is worth confirming the current status of the specific capabilities being considered. 

Are VTEX AI Solutions Only for B2C Sales? 

No. VTEX supports B2C, B2B, D2C, marketplace, and omnichannel models. Individual AI capabilities can support both consumer purchases and processes specific to business-to-business sales. 

Can AI Agents Change Prices and Product Offerings Autonomously? 

Their level of autonomy depends on the configuration and the rules adopted by the organization. A business can decide which actions an agent performs automatically, which it only recommends, and which require employee approval. 

What Data Is Needed to Use AI in E-Commerce? 

This depends on the use case. In most cases, businesses need current product data, prices, and information about inventory, promotions, orders, and customer behavior. The volume of data matters, but its quality, consistency, and regulatory compliance are equally important.