As people grow more comfortable asking questions online through large language models (LLMs) and voice assistants, digital shopping is quickly becoming more conversational. While traditional site search remains critically important, advances in AI have opened the door to a richer product discovery experience — one that replicates the helpfulness of speaking to an in-store associate and augments it with the comprehensive knowledge and speed of the machine.
Constructor’s AI Shopping Agent (ASA) and AI Product Insights Agent (PIA) are built for this emerging form of ecommerce interaction, providing helpful recommendations, surfacing relevant content, and answering questions in real time. Whether implemented through search bars, chat-style interfaces, or PDP widgets, our AI agents guide users from curiosity to confident decision-making.
Behind the scenes, our agentic AI solutions agents are deeply tuned to your catalog and your shoppers — leveraging real-time clickstream data, sentiment cues, and LLM-driven reasoning to deliver responses that are not only accurate, but also helpful. We’ve trained our ecommerce-focused AI models on petabytes of data from real online shoppers so our agents leverage the best of your own data and the “wisdom of the stores.”
The result? Smarter discovery, higher engagement, and a shopping experience that feels more intuitive, contextual, and human.
From Intent to Action: The Role of the AI Shopping Agent
Picture this: a shopper lands on your website and types, "I'm training for a triathlon. What gear do I need?" or "I want to make a homemade pizza." Traditional search engines struggle with these natural language, open-ended conversational queries. They’re only built to match keywords to product data in a search index.
ASA is designed to capture and respond to broad user intents, offering tailored product suggestions, content, and categories based on a combination of:
- Uploaded structured data (products, attributes, categories)
- Unstructured content (recipes, articles, manuals)
- Behavioral signals (preferences, past purchases, popularity)
Instead of asking shoppers to do the work, ASA does the cognitive heavy lifting. It identifies relevant groupings (such as wetsuits, bike gear, and running shoes for triathlon prep) and assembles personalized results accordingly. If content such as training tips or recipe articles is available, ASA can also surface those, enriching the experience with contextual knowledge.
Importantly, ASA adapts to the data available. If a specific recipe or guide isn’t present, the LLM can fall back on its core knowledge base to generate useful answers, like listing common carrot cake ingredients or suggesting categories of outdoor gear.
Personalization at the Point of Discovery
Where ASA stands out is not just in surfacing results, but in personalizing them in real time. Suppose a customer is known to prefer organic ingredients. ASA can prioritize organic options when recommending toppings for a pizza recipe. If they have recently purchased a specific bike, it can suggest compatible accessories based on the frame size and their previous purchases.
Beyond behavioral data, ASA also interprets signals from the language of the query itself. Through built-in sentiment analysis, it can detect urgency, uncertainty, or preference strength, like a shopper saying, “I really need a non-dairy option” versus “I’m just browsing for fun,” and adjust its recommendations and tone of response accordingly. The result is a more context-aware interaction that helps shoppers feel understood, not just served.
Deep Context at the Point of Decision: The AI Product Insights Agent (PIA)
When shoppers reach a product detail page (PDP), they need fast, clear answers about whether the product fits their needs. Time on site is decreasing, bounce rates are rising, and purchase decisions are increasingly made — or abandoned — within seconds. Most PDPs do little to resolve doubt or accelerate confidence.
This is where PIA shines. While ASA handles the top-of-funnel journey, the AI Product Insights Agent comes into play once a shopper clicks on a product or enters your site directly through a PDP.
Embedded directly in the PDP layout (where it appears on the page is up to you), PIA is an LLM experience that enables shoppers to ask highly specific questions and get trustworthy answers instantly. This means no more on-page Q&A forms that may never get answered, or waiting for a human agent to join a chat.
Examples of common product questions include:
- Suitability: "Is this good for oily skin?" "Will it support dual monitors?"
- Details: "How easy is this fabric to clean?" "Is the SPF mineral or chemical?"
- Comparisons: "Which processor chip is better for advanced video editing?" "Is this better for babies than product XYZ?"
- Suggestions: "What else can I pair this with?" "Does this come in blue?"
These aren’t hardcoded FAQs. They’re dynamic, intelligent answers powered by a flexible answering pipeline that draws from a variety of sources:
- Product specs and metadata
- Knowledge base articles and manuals
- Customer reviews
- Similar items in the catalog
- Our foundation model’s underlying knowledge of the world
For example, if a shopper asks whether a bookshelf requires two people to assemble, the agent can pull from a PDF manual or FAQ. If they ask how a jacket compares to a previous model, it might look at reviews or product tags. If they want a version in a different color, it can search the catalog for variants and show a tile with recommendations.
Guided Search, Not Guesswork
One of the agent’s key strengths is its ability to guide users proactively. Pre-generated prompts appear based on common questions about the product, informed by aggregate behavioral data. Over time, these prompts are optimized for engagement — automatically re-ranking based on which ones are clicked most often.
PIA can suggest follow-up questions for both pre-generated and user-submitted queries. These follow-ups help shoppers explore topics more deeply or discover related information.
This tight feedback loop between user intent and product education shortens the decision-making cycle and boosts confidence. Shoppers don’t need to leave the PDP to get answers. They get them right where and when they matter most.
Design Flexibility Meets Conversational Intelligence
Constructor’s agents are modular and embeddable. ASA can operate through a universal search bar or as a dedicated chatbot, depending on your preference (and we recommend A/B testing with your own customers).
This luxury home goods brand has ASA embedded directly into their category page, making it easy for shoppers to find the perfect coffee maker or espresso machine for their needs.
Similarly, the Product Insights Agent can appear as an expandable widget, an inline Q&A block, or via custom UI integrations.
This lighting retailer has PIA embedded as an inline Q&A block, which is easily accessible from the PDP.
We provide a UI library to accelerate deployment, but you can render these interactions however you choose, and even extend them outside your catalog to mobile apps, in-store kiosks, shoppable content, and wearable devices using our API.
Constructor delivers the intelligence; you own the experience.
Domain-Specific Product Guidance
For retailers who carry a wide range of product types, our architecture supports the creation of domain-specific agents tailored to specific categories, such as skincare, technical apparel, sporting equipment, or niche electronics.
By combining LLMs with structured product data, domain-specific taxonomies, and curated prompt engineering, these agents can be trained to understand the nuances of a given category. For example, a skincare agent might recognize ingredient sensitivities or routines, while a bike gear agent could assist based on fit, frame size, terrain, and rider experience level.
This specialization enables more accurate responses, more relevant recommendations, and a more trusted advisor-like experience for the shopper.
Unified Data, Unified Experience
Another key advantage: ASA and PIA are integrated with our unified ecosystem. When deployed together with Search & Discovery, these agents benefit from a shared understanding of:
- Shopper profiles and affinities
- Real-time catalog and inventory data
- Historical engagement trends
Conversational context feeds back into the user’s profile and enhances all search, browse, and recommendation experiences for holistic personalization.
The Takeaway: Agentic AI Is the New UX
For ecommerce teams, agentic AI isn’t just a feature. It’s a shift in user experience design. Instead of building rigid flows and hoping customers find what they’re looking for, we can now meet them in conversation. Conversations with agentic AI can guide shoppers, teach them, and personalize every step of the way.
Constructor’s ASA and AI PIA exemplify this shift, turning search and PDPs from passive components into intelligent collaborators. Like speaking to a knowledgeable store associate, our agents give shoppers the confidence and assurance to convert and come back, leading to higher revenue, lifetime value, and brand trust.
As ecommerce moves beyond the search bar, agentic AI becomes the connective tissue between curiosity and conversion. And this is just the beginning. At Constructor, we’re continually investing in AI innovations for next-level ecommerce experiences.
Ready to get agentic? Discover how this emerging technology can help you achieve your key business objectives.