In the same way tools like ChatGPT and Perplexity have redefined search, shoppers now expect conversational, context-aware guidance everywhere they browse, including your site. Over half of Americans have used ChatGPT, and one-third use it several times a week for all types of queries, including shopping research.
This shift is costing retailers whose sites are too rigid for today’s dynamic shopper mindset.
GenAI has the power to remove an enormous amount of friction from search and discovery, especially at the point of decision. When shoppers reach a product detail page (PDP), they need fast, clear answers about whether the product fits their needs. If their questions aren’t answered by static description content or even product reviews, they simply bounce, abandoning your site within seconds.
That’s why we’re excited to unveil the newest addition to our Agentic Suite, which gives retailers the intelligence infrastructure to match — and exceed — the expectations set for product discovery by LLM-native interfaces.
Our new PDP Q&A solution, AI Product Insights Agent (PIA), meets shoppers with fast, personalized answers that enable confident, informed decisions without friction or delay.
AI Product Insights Agent (PIA) is an LLM-powered chat agent that delivers instant answers to final product questions directly on PDPs.
The PIA widget can be embedded anywhere within your PDP template to enable an interactive chat experience through which shoppers can ask their own questions or choose from auto-generated “frequently asked prompts” related specifically to the product being viewed.
It then suggests follow-up questions for both pre-generated and user-submitted queries. These follow-ups help shoppers explore topics more deeply or discover related information.
By proactively surfacing relevant questions, PIA reduces the cognitive load on shoppers — who may find it challenging to generate questions themselves — and helps maintain user engagement.
To see the real value for retailers and shoppers, we break down how PIA works below.
There are several factors that determine how the pre-populated prompts are chosen.
The agent digests all your product information (including images and metadata), FAQ blocks, size charts, buyer’s guides, blog posts, customer reviews, and any additional resources you want to feed it, such as manufacturer documents. The agent can also access off-site information sources to enrich its answers.
Initially, the agent generates a number of questions and serves them evenly across page views to capture engagement data from real users. Over time, it learns which questions are frequently clicked and which correlate to conversions and revenue.
PIA also learns from users' questions to improve topic modeling and cover more topics in the next round of question generation.
Showing proactive prompts also helps customers understand what they should be thinking about related to the product being viewed. Shoppers often “don’t know what they don’t know” about what they’re buying, especially when it’s their first purchase of the kind or when products have a large set of features and specifications.
Like a helpful in-store sales associate that’s full of product knowledge and skilled at asking customers the right questions, suggesting “probing questions” through pre-made prompts or chat replies highlights the buying criteria that should qualify or disqualify a product for the individual user.
For example:
As the agent asks qualifying questions of the user, it can better understand how to provide in-context information in real time (which is something static product descriptions can never do).
Plus, PIA has memory, remembering all questions submitted by shoppers on the same PDP. The agent takes specific context into account to generate the next answer and suggest relevant follow-up questions.
PIA can surface both complementary and alternative items right inside the chat, making for a more precise product discovery experience.
When a shopper asks, “What else goes well with this?” or “Show me similar options,” the widget passes the request (plus the product-page context and any relevant metadata) to Constructor Recommendations.
The Recommendations engine returns a candidate list, and the LLM then filters and ranks those results so that only the most on-point suggestions appear in the conversation.
Behind the scenes, a lightweight “query understanding” service decides which recommendation strategy to invoke. Depending on the wording, it can call the Complementaries strategy, the Alternatives strategy, or both at once.
The result is fast, context-aware bundling and substitution advice without the need for extra merchandiser setup.
PIA can also enhance your customers' ownership experience by answering post-purchase questions like “what’s the best detergent to use with this fabric?” and providing technical troubleshooting tips.
Shoppers also frequently ask questions like “What’s your return policy?” We can handle these inquiries directly or, at the very least, refer them to the appropriate resource on the site.
You may consider sending a post-purchase email that requests a product review and reminds your customer that they can return to the product page at any time for additional questions they may have (with a link).
You can even tag this URL with parameters that tell your website to serve a recommended products section near the PIA widget to spur an accessory or related product purchase.
This new inline LLM solution allows retailers to:
Also, when retailers take the full agentic approach to modern commerce and pair PIA with our AI Shopping Agent (ASA), shoppers receive AI-first guidance from the moment they land on-site to the second they click “add to cart” — on your terms, not an external LLM’s.
As AI-native shopping becomes the norm, retailers who don’t embed this experience risk losing influence to external platforms — and the sale along with it.
By placing the AI Product Insights Agent directly on your product pages, you give every shopper an instant, knowledgeable companion that removes the last bits of friction in their decision-making journey, boosting their confidence in clicking “buy.”
Throughout that journey, PIA does more than answer questions. It interprets each request in real time, surfaces hyper-relevant details from your own content, and even curates complementary or alternative products when the moment is right. The best part is your merchandisers don’t have to move a finger once fully implemented. And PIA can continue the conversation after checkout, too, with ownership tips and troubleshooting guidance that keep customers satisfied long after the sale.
If you’re ready to turn static PDPs into living, revenue-driving dialogues, it’s time to see PIA in action. Reach out for a demo to see how running a pilot on your highest-traffic product pages will benefit shoppers and your bottom line.