Selling inside the conversation: How LLMs are becoming the next storefront

Every era of ecommerce has had its own race for visibility. From the top spots in marketplace listings to the first page of search results, and now to recommendations by large language models (LLMs).

More than half, almost 58% of consumers today have replaced traditional search with LLMs for product or service recommendations. And, this shift isn't slowing down.

LLM conversations are becoming the first touchpoint in a modern buyer's journey, replacing your ads, blogs, or even a well-optimized landing page. eCommerce brands that don't adapt to this risk staying completely invisible to potential customers.

This guide explores how LLMs are selling inside their conversations and how brands should prepare in order to earn meaningful recommendations.

What is conversational commerce in LLMs?

Conversational commerce in LLMs refers to the use of large language models to help customers discover, compare, evaluate, and even purchase products through natural language conversations.

Instead of relying on search engines, shoppers can describe their needs in plain language and receive personalized product recommendations according to their preferences.

Behind the scenes, LLM-powered shopping assistants retrieve relevant information from product catalogs and other online sources. They reason across multiple options to recommend the most suitable products.

How does an LLM sell inside a conversation?

Unlike the traditional search that relied on keywords, LLMs understand intent. It generates a response by reasoning over the user's intent, available knowledge, and also external product data.

1. LLMs first understand the user's intent

Every conversation begins by converting natural language into structured intent. For example, just like in the conversation above, the model doesn't just see the keywords, it identifies several constraints simultaneously:

• Product category → Smartwatch

• Budget → Under $200

• Primary use case → Running

• Possible priorities → GPS, heart-rate tracking, battery life

• Implicit goal → Recommend products rather than explain smartwatches

2. It builds context as the conversation evolves

A LLM conversation is cumulative and every new message is added to the context window.

For example, statements like "I use Android" or "To me, battery life matters more than advanced fitness metrics" updates the recommendation because the model considers both the new information and the earlier conversation when generating the next recommendation.

3. It retrieves relevant product information

LLMs are not reliable sources of live product data and therefore they use retrieval-augmented generation (RAG).

Before generating a response, the application converts the user's request into an embedding and performs a semantic search over external knowledge sources, such as:

• Product catalogs

• Technical specifications

• Inventory databases

• Customer reviews

• Brand documentation

• eCommerce APIs

4. It reasons across multiple products

Retrieving products is only half the process. LLMs also determine which options satisfy the user's requirements best.

Using the retrieved information and conversation context, the LLM performs comparative reasoning by weighing across attributes.

This is why two users can receive the same product recommendation for two entirely unlike reasons.

5. It grounds recommendations with external tools

When a LLM needs information it doesn't have, such as current pricing, inventory, delivery estimates, or store availability, it can invoke external APIs instead of guessing.

For example, during a shopping conversation, the application may call:

• A product search API

• A pricing service

• An inventory management system

• A shipping calculator

• A review database

The returned data is inserted back into the model's context, enabling responses that are both conversational and grounded in live business information.

Why are shoppers moving towards this shift?

Conversational shopping works largely in favor of online customers; it is expected that more than 800 million will shop inside LLMs. There are several factors influencing this, with the main ones being time-saving and convenience.

LLMs save online shoppers from comparison fatigue

One of the biggest frustrations for online customers is comparing between different products or services. Opening multiple tabs, reading through specifications, and watching reviews on YouTube can be tiring and confusing.

LLMs help shoppers by shortlisting the best results. It evaluates options based on the user's exact requirements and returns only the most relevant recommendations.

AI reduces significant time and effort in a modern buyer's journey

The traditional buying journey often looks like this:

But the AI customer journey reduces as follows:

With conversational commerce, the research happens within a single interaction. Current data reveals that customers engage in just six conversational prompts before leaving for an ecommerce site to complete their transaction.

Guided recommendations help make confident decisions

The real struggle for most online shoppers lies not in finding the information, but coming to a confident conclusion.

That's what LLMs are particularly good at. Instead of just presenting details, they interpret them in the context of the shopper's goals and guide them through detailed explanations, just like an experienced sales assistant.

This helps online customers get a clearer picture and lets them make confident decisions.

The two sides of chat commerce

Like every major shift in technology, conversational commerce is both an opportunity and a challenge.

For shoppers, LLMs are an extremely convenient alternative to manually researching, comparing, and making buying decisions.

For businesses, however, the shift is more nuanced.

Better reach

Brands have a new channel to reach customers. A Harvard article points out that US retail stores saw a 1,300% increase in AI search referrals during the holiday shopping season.

Higher conversion rate

Search Engine Land also found that traffic originating from LLMs converts at around 18%, significantly outperforming traditional acquisition channels such as paid advertising, SEO, and pay-per-click (PPC).

Non-brand controlled environment

Brands lose a lot of control over the customer experience, how they are portrayed, and if they are being recommended to potential customers.

Ads within LLMs

Adding to the challenge, AI platforms such as ChatGPT, Gemini, and Perplexity are beginning to explore advertising and sponsored recommendations. This means brands will need to compete for both organic AI visibility and paid placement within these new shopping experiences.

Hesitation on trustworthiness

There are also challenges around accuracy. A study by Semrush revealed that almost 86% of shoppers verify the recommendations by LLMs before going forward with a purchase, showing the hesitation that's still revolving around LLMs' trustworthiness. LLMs can misunderstand user intent, rely on outdated information, or generate incorrect responses.

While LLM commerce has yet to replace traditional search, brands can no longer afford to ignore optimizing for it. In the second half of 2025 alone, referral traffic from LLMs grew by 80%, signaling that more shoppers are beginning their buying journey with AI.

How can you influence an LLM to recommend you within conversations?

Optimizing for LLMs is called generative engine optimization (GEO). Fine-tuning your content for LLMs is not a guaranteed way to for a recommendation, but significantly improves the chances. Here are some tips for GEO.

Publish complete and structured product data

LLMs can only reason with the information they have access to. Rich product pages should include more than a title and a short description. They should clearly specify:

• Features and specifications

• Dimensions and materials

• Compatibility

• Use cases

• Benefits and limitations

• Warranty information

• FAQs

The more structured and comprehensive your product information is, the easier it is for AI systems to retrieve and compare it accurately.

Explain who the product is for

Customers rarely search by product name, they describe a problem. Instead of just explaining: "Running Shoe X has EVA foam," describe it like "Has EVA foam and is designed for long-distance runners who need extra cushioning for marathon training."

This gives LLMs the context they need to match your product with a user's intent.

Make your content machine-readable

In generative AI ecommerce, the information is retrieved directly from websites, APIs, product feeds, and structured databases.

Using structured data such as Schema.org markup, consistent product attributes, clean HTML, and well-organized catalogs makes it easier for retrieval systems to identify and understand your products.

Well-structured data also reduces ambiguity, helping AI systems distinguish your products from similar alternatives.

Build authority beyond your website

LLMs don't rely on a single source of information. They may retrieve or reference information from:

• Product documentation

• Editorial reviews

• Industry publications

• Community discussions

• Public knowledge bases

• Brand websites

When your products are consistently described across trusted sources, AI systems have stronger evidence to support recommending them.

Keep your information current

Prices, specifications, availability, and product catalogs change frequently. Providing up-to-date product feeds, APIs, or frequently refreshed catalogs helps ensure AI assistants recommend accurate information instead of outdated details.

Closing thoughts

For most of ecommerce history, the storefront was something you built and controlled. Now, it's becoming something you can only influence, like a sentence a model writes about you, inside a conversation you'll never see.

As a brand, all you need to focus on at this point is to make sure that when the model reaches for facts about your store, it finds clear, specific data and describes you accurately to someone ready to buy.