LLM-Enhanced E-commerce Agentic Assistant Part 1 (Text-Based)

In the fast-paced world of e-commerce, customers care about three key things: getting the best price, discovering the latest products, and receiving orders quickly. During our daily operations, we encountered challenges gathering this critical information from vast product catalogs. Motivated by our curiosity and the need to streamline this process, we developed a testing prototype of the LLM-Enhanced E-commerce Agentic Assistant.

Customer-Centric Approach: Insights from User Research

Our journey began with user research to truly understand our customers’ needs. We discovered that:

• Best Price: Customers are always on the lookout for better deals.
• Latest Offerings: Shoppers want to know about the newest products as soon as they are available.
• Fast Delivery: Delivery time is a major factor in purchase decisions.

This deep understanding drove us to create a prototype that directly addresses these concerns. When a customer inquires about a product—say, “How much is the iPhone 14 Plus?”—our prototype dynamically retrieves real-time information including current pricing, available discounts, and delivery options.

How Our Testing Prototype Works

1. Agentic Workflow with OpenAI:
   We leverage the power of OpenAI’s model to call our integrated e-commerce tools. When a query is made, the model triggers the relevant tool, which then pulls live data on discounts, product pricing, and delivery options.
2. Data Integration:

• Real-Time Updates: The assistant integrates with e-commerce data to pull updates on discounts, product pricing, and delivery options.
• Smart Suggestions:
  • If the queried product isn’t available in our current listings, the prototype suggests subscribing to our newsletter for future updates.
  • If the product lacks active discounts, it recommends keeping an eye out for upcoming shopping events.

Why It Matters

This testing prototype aims to solve a critical problem: customers waste too much time scouring multiple pages for essential product information. By automating data retrieval through a streamlined, agentic workflow, we deliver instant, actionable answers. This means customers can quickly decide whether a product meets their needs without the hassle of manual searching.

Conclusion

Our LLM-Enhanced E-commerce Agentic Assistant prototype is a direct response to everyday challenges in e-commerce. Using OpenAI’s model to intelligently call our data tools, we are paving the way for a more efficient, customer-centric shopping experience. Stay tuned as we continue to refine and expand our prototype into a fully-fledged solution.