Using Agentic RAG to Transform Retail With MongoDB

In the competitive world of retail and ecommerce, it’s more important than ever for brands to connect with customers in meaningful, personalized ways. Shoppers today expect relevant recommendations, instant support, and unique experiences that feel tailored just for them.

Enter retrieval-augmented generation (RAG): a powerful approach that leverages generative AI and advanced search capabilities to deliver precise insights on demand. For IT decision-makers, the key challenge lies in integrating operational data with unstructured information—which can span object stores (like Amazon S3 and SharePoint), internal wikis, PDFs, Microsoft Word documents, and more. Enterprises must unlock value from curated, reliable internal data sources that often hold critical yet hard-to-access information. By combining RAG’s capabilities with these data assets, retailers can find contextually accurate information. For example, they can seamlessly surface needed information like return policies, refund processes, shipment details, and product recalls, driving operational efficiency and enhancing customer experiences.

To provide the most relevant context to a large language model (LLM), traditional RAG (which has typically relied on vector search) needs to be combined with real-time data in an operational database, the last conversation captured in a customer relationship management API call to a REST endpoint, or both. RAG has evolved to become agentic—that is, it’s capable of understanding a user inquiry and translating it to determine which path to use and which repositories to access to answer the question.

MongoDB Atlas and Dataworkz provide an agentic RAG as a service solution that enables retailers to combine operational data with relevant unstructured data to create transformational experiences for their customers. MongoDB Atlas stores and unifies diverse data formats—such as customer purchases, inventory levels, and product descriptions—making them easily accessible. Dataworkz then transforms this data into vector embeddings, enabling a multistep agentic RAG pipeline to retrieve and create personalized, context-aware responses in real time. This is especially powerful in the context of customer support, product recommendations, and inventory management.

When customers interact with retailers, Dataworkz dynamically retrieves real-time data from MongoDB Atlas, and, where needed, combines it with unstructured information to generate personalized AI responses, enhancing the customer experience. This architecture improves engagement, optimizes inventory, and provides scalable, adaptable AI capabilities, ultimately driving a more efficient and competitive retail operation.

Reasons for using MongoDB Atlas and Dataworkz

MongoDB Atlas and Dataworkz work together to deliver agentic RAG as a service for a smarter, more responsive customer experience. Here’s a quick breakdown of how:

• Vector embeddings and smart search: The Dataworkz RAG builder enables anyone to build sophisticated retrieval mechanisms that turn words, phrases, or even customer behaviors into vector embeddings—essentially, numbers that capture their meaning in a way that’s easy for AI to understand—and store them in MongoDB Atlas. This makes it possible to search for content based on meaning rather than exact wording, so search results are more accurate and relevant.

• Scalable, reliable performance: MongoDB Atlas’s cloud-based, distributed setup is built to handle high-traffic retail environments, minimizing disruptions during peak shopping times.

• Deep context with Dataworkz’s agentic RAG as a service: Retailers can build agentic workflows powered by RAG pipelines that combine lexical and semantic search with knowledge graphs to fetch the most relevant data from unstructured operational and analytical data sources before generating AI responses.

This combination gives ecommerce brands the power to personalize experiences at a vastly larger scale.

Figure 1: Reference architecture for customer support chatbots with Dataworkz and MongoDB Atlas

Retail e-commerce use cases

So how does this all work in practice? Here are some real-world examples of how MongoDB Atlas and Dataworkz are helping ecommerce brands create standout experiences.

Building smarter customer-support chatbots

Today’s shoppers want quick, accurate answers, and RAG makes this possible. When a customer asks a chatbot, “Where’s my order?” RAG enables the bot to pull the latest order and shipping details stored in MongoDB Atlas. Even if the question is phrased differently—say, “I need my order status”—the RAG-powered vector search can interpret the intent and fetch the correct response. As a result, the customer gets the help they need without waiting on hold or navigating complex menus.

Personalizing product recommendations

Imagine a customer who’s shown interest in eco-friendly products. With MongoDB Atlas’s vector embeddings, a RAG-powered system can identify this preference and adjust recommendations accordingly. So when the customer returns, they see suggestions that match their style—like organic cotton clothing or sustainably sourced kitchenware. This kind of recommendation feels relevant and thoughtful, making the shopping experience more enjoyable and increasing the chances of a purchase.

Creating dynamic marketing content

Marketing thrives on fresh, relevant content. With MongoDB Atlas managing product data and Dataworkz generating personalized messages, brands can send out dynamic promotions that truly resonate. For example, a customer who browsed outdoor gear might receive a curated email with top-rated hiking boots or seasonal discounts on camping equipment. This kind of targeted messaging feels personal, not pushy, building stronger customer loyalty.

Enhancing site search experiences

Traditional e-commerce searches often rely on exact keyword matches, which can lead to frustrating dead ends. But with MongoDB Atlas Vector Search and Dataworkz’s agentic RAG, search can be much smarter. For example, if a customer searches for “lightweight travel shoes,” the system understands that they’re looking for comfortable, portable footwear for travel, even if none of the product listings contain those exact words. This makes shopping smoother and more intuitive and less of a guessing game.

Understanding trends in customer sentiment

For e-commerce brands, understanding how customers feel can drive meaningful improvements. With RAG, brands can analyze reviews, social media comments, and support interactions to capture sentiment trends in MongoDB Atlas. Imagine a brand noticing a spike in mentions of “too small” in product reviews for a new shoe release—this insight lets them quickly adjust sizing info on the product page or update their stock. It’s a proactive approach that shows customers they’re being heard.

Interactions that meet customers where they are

In essence, MongoDB Atlas and Dataworkz’s RAG models enable retailers to make e-commerce personalization and responsiveness smarter, more efficient, and easier to scale. Together, they help retailers deliver exactly what customers are looking for—whether it’s a personalized recommendation, a quick answer from a chatbot, or just a better search experience.

In the end, it’s about meeting customers where they are, with the information and recommendations they need. With MongoDB and Dataworkz, e-commerce brands can create that kind of connection—making shopping easier, more enjoyable, and ultimately more memorable.