How to Build an AI Agent for Product Recommendations

In today's fast-paced e-commerce industry, product recommendations are more important than ever. With countless options available, customers expect personalized suggestions that make shopping easier and more relevant. Smart recommendation systems enhance user experience, increase sales, and boost customer retention by showing the right products at the right time. From AI-driven recommendations to tailored shopping experiences, businesses that leverage smart suggestions gain a competitive edge. In a world where convenience is key, effective product recommendations can turn browsing into buying and first-time shoppers into loyal customers.

What Is an AI Agent for Product Recommendations

Traditionally, product recommendations relied on basic rules, manual curation, or simple filters** like bestsellers and category-based suggestions. These methods lacked personalization and often failed to capture individual customer preferences. But today, with AI revolutionizing nearly every industry, product recommendations have become smarter, more dynamic, and deeply personalized. AI-powered agents analyze real-time user behavior, purchase history, and even browsing patterns to predict what customers are most likely to buy. Instead of one-size-fits-all suggestions, AI delivers highly relevant recommendations across websites, emails, and chat interactions—boosting engagement, increasing conversions, and creating a seamless shopping experience.

In this blog, we'll walk you through the step-by-step process of creating and deploying an AI agent for product recommendations, making it easy to enhance your e-commerce experience with smart, personalized suggestions.

Build an AI Agent for Product Recommendations with Recomi

Before we begin, ensure you have prepared the data that will be used to generate recommendations based on your customers' queries. Having well-structured data is key to providing accurate and relevant product suggestions.

The dataset used in the following example consists of the top 100 book reviews on Amazon, available on Kaggle. If you don't have your own data ready, you can try the example dataset to follow along with the process.

The AI agent builder we use is Recomi, an no-code AI agent building platform offered by Powerdrill.

Step 1. Sign in to Recomi

Sign in to Recomi with your Email.

Step 2. Upload Your Files and Set Up Your AI Agent

1. Click Create Agent or Create New Agent on the page. 

   

   
   

2. Set a name for your agent, for example, Book Recomi, and upload your data files, and set some preferential settings.

   

   
   

   You can also customize the chat UI on the Settings tab.

   

   
   

3. Now, you can ask some questions to test whether your AI agent works well.

   For example, try "What are the top 10 books and their genres?"

   

   
   

   Let's try another one: "I want to buy a memoir, and my budget is $10. Give me your recommendations."

   

Step 3. Integrate the AI Agent with Your Website

Click the Integration tab, select your preferred embedding method, and follow the guide to complete the integration.

Conclusion

Ready to enhance your customer experience with AI-driven recommendations? Try Recomi today and seamlessly embed AI Agents into your website or app to provide smart, dynamic, and personalized interactions.