When people look for certain products or services online, they often want to compare brands or models to know the difference between them.
The standard website experience requires you to click back and forth between pages and try to remember what you just saw.
Take running shoes for example. You may compare the weight of the shoes in ounces, the heel to toe drop in millimeters, the cushion (low to high), the support (neutral versus stability), and more. All details that matter to runners.
Or credit cards. You could compare the annual fee, the sign-up bonus, and the different rewards. You also may see that two credit cards both offer “cash back,” but you spend the most on groceries, so you want to see which one maximizes rewards there.
Brands can now use an AI agent to transform these web browsing experiences (thinking clicking from page to page, filtering, and memorizing) into more natural conversations.
With a single item question, you might ask: “Are these shoes waterproof?” The agent uses retrieval-augmented generation (RAG) to pull the most relevant details from that brand's content. It finds the details and explains them back in a concise summary or description.
But if you’re comparing two or more products, you can ask questions like “what are the main differences,” or be more specific asking to compare the cooking capacity, the heating performance, the smart features, etc. just as you would ask a store representative.
In both cases, the AI is providing a response from the brand’s content and adapting it to fit the user who’s asking. The only change is whether it’s looking at one thing in isolation or analyzing multiple together.
Comparisons are often the point where shoppers get stuck. It’s when they’re closest to a decision, but also most likely to hesitate. Giving them clarity at that moment can make or break a purchase, and can also be the difference between a satisfied customer and a returned product.