AI-Powered Retail With Together AI and MongoDB

Generative AI (gen AI) is changing retail in fascinating ways. It’s providing new avenues for IT leaders at retailers to enhance customer experiences, streamline operations, and grow revenue in a fast-paced environment. Recently, we’ve been working closely with a fascinating organization in this space—Together AI. In this blog, we’ll explore how Together AI and MongoDB Atlas tremendously accelerated the adoption of gen AI by combining the capabilities of both platforms to bring high-impact retail use cases to life.

Introduction to Together AI and MongoDB Atlas

From the first look, it’s impressive how well Together AI is designed for gen AI projects. It’s a powerful platform that lets developers train, fine-tune, and deploy open-source AI models with just a few lines of code. This is a critical component for retrieval-augmented generation (RAG). With RAG, AI can pull real-time business-specific data from MongoDB Atlas, which means retailers get more reliable and relevant outputs. That’s crucial when dealing with data as dynamic as customer behavior or inventory movement from online and physical stores.

With its flexible data model, MongoDB Atlas is an ideal database engine for handling diverse data needs. It’s fully managed, multi-cloud, and exceptional at managing different data types, including the vector embeddings that power AI applications. One important feature is MongoDB Atlas Vector Search, a smart library that stores and indexes vector embeddings, making it simple to integrate with Together AI. This lets retailers generate timely, personalized responses to customer queries, creating a better experience all around.

Identifying retail use cases

With Together AI and MongoDB Atlas working together, the possibilities for retail are huge. Here are some of the use cases we’ve been exploring and testing with clients, each bringing measurable value to the table:

Product description generation

Product onboarding to a retail e-commerce portal is a time-consuming effort for many retailers. They need to ensure they’ve created a product description that matches the image, then deploy it to their e-commerce portal. For multilingual portals and multiple operating geographies, this challenge of accuracy increases. With Together AI’s support for multimodal models (e.g. Llama 3.2) and MongoDB Atlas’s vector embeddings, we can create accurate product descriptions in multiple languages.

Check out a demo app to see it in action.

Personalized product recommendations

Imagine being able to offer each customer exactly what they’re looking for, without them even asking. With Together AI’s retrieval and inference endpoints and MongoDB Atlas Vector Search, we can create highly personalized product recommendations. Analyzing individual preferences, browsing history, and past purchases becomes seamless, giving customers exactly what they need, possibly exactly when they need it.

Conversational AI-powered tools (a.k.a. chatbots)

We’re also deploying intelligent conversational tools that can understand complex questions, offer personalized assistance, and drive conversions. Together AI, paired with MongoDB Atlas, makes these bots responsive and relevant so customers feel like they’re talking to a knowledgeable adviser rather than a chatbot. When real-time data informs the responses, customer experience is enhanced.

Dynamic pricing and promotions

Pricing in retail is often a moving target, and AI-driven insights help us optimize our approach. We’ve used Together AI and MongoDB Atlas to analyze market trends, competitor pricing, and customer demand to keep our pricing competitive and adjust promotions in real-time. It’s incredible how much more strategic we can be with AI’s help.

Inventory management and forecasting

This might be one of the most impactful use cases I’ve worked on—using AI to predict demand and optimize stock levels. With Together AI and MongoDB Atlas, it’s easier to balance inventory, reduce waste, and ensure the products customers want are always in stock. This leads to better efficiency and fewer out-of-stock scenarios.

Implementing retail use cases with Together AI and MongoDB Atlas

Let me share a concrete example that really brings these concepts to life.

Case study: Building a multilingual product-description-generation system

We recently worked on a solution to create a product-description-generation system for an e-commerce platform. The goal was to provide highly descriptive product information based on the images of the products from the product catalog. This use case really demonstrated the value of storing the data in MongoDB and using the multilanguage capabilities of Together AI’s inference engine.

• Embeddings and inference with Together AI: Together AI generated product descriptions based on images retrieved from the product catalog using Llama 3.2 vision models. This way, each product’s unique characteristics were considered, then generated in multiple languages. These descriptions could then be embedded into the MongoDB Atlas Vector Search database via a simple API.

• Indexed embeddings with MongoDB Atlas Vector Search: Using MongoDB Atlas Vector Search capabilities, we created embeddings, and then indexed them to be used to retrieve relevant data based on other matched product queries. This step made sure the product descriptions were not just accurate but also relevant to the images.

• Real-time data processing: By connecting this setup to a real-time product dataset, we ensured that product descriptions in multiple languages were always updated automatically. So when a marketplace vendor or retailer uploads new images with distinct characteristics, they get up-to-date product descriptions in the catalog.

This project showcased how Together AI and MongoDB Atlas could work together to deliver a solution that was reliable, highly efficient, and scalable. The feedback from users was overwhelmingly positive. They especially appreciated how intuitive and helpful the product descriptions were and how simple the whole product onboarding process could become for multilingual businesses spread across multiple geographical regions.

Looking at the business impacts

For a retail organization, implementing Together AI and MongoDB Atlas can streamline the approach to gen AI, creating an effective and immediate positive impact to business in several ways:

• Reduced product onboarding time and costs: Retailers can onboard products faster and quickly make them available on their sales channels because of the ready-to-use tools and prebuilt integrations. This cuts down on the need for custom code and significantly lowers development costs.

• Increased flexibility and customization: MongoDB’s flexible document model and Together AI’s inference engine enables retailers to mold their applications to fit specific needs, such as back-office data processing, demand forecasting, and pricing as well as customer-facing conversational AI.

• Seamless integration with existing systems: MongoDB Atlas, in particular, integrates seamlessly with other frameworks we’re already using, like LangChain and LlamaIndex. This has made it easier to bring AI capabilities to adopt across various business units.

• Added support and expertise: The MongoDB AI Applications Program (MAAP) is especially helpful in beginning the journey into AI adoption across enterprises. It offers not just architectural guidance but also hands-on support, so enterprises can implement AI projects with confidence and a well-defined road map.

Combining Together AI and MongoDB Atlas for a powerful approach to retail

Together AI and MongoDB Atlas are a powerful combination for anyone in the retail industry looking to make the most of gen AI. It is evident how they help unlock valuable use cases, from personalized customer experiences to real-time operational improvements. By adopting MongoDB Atlas with Together AI, retailers can innovate, create richer customer interactions, and ultimately gain a competitive edge. If you’re exploring gen AI for retail, you’ll find that this combination has a quick, measurable, and transformative impact.

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