The AI Shopping Assistant That Increased Add-to-Carts By Nearly 10x

Trail Appliances—one of Canada’s leading home appliance retailers—knew this experience too well. With hundreds of models and nuanced differences between them, they wanted to simplify the digital shopping journey. So they partnered with Orium to pilot a new kind of solution: a multi-agent storefront AI assistant designed to guide shoppers through complex purchase decisions, helping them answer things like:

• What are “adjustable tines”, and do they matter when buying a new dishwasher?
• How important is it that my washer be “Energy Star Certified"?
• What does paying $200 more get me— and is it worth it?

Instead of leaving customers to research on their own, the assistant would leverage multiple sources of information—structured product data, FAQs, policies, and trusted knowledge bases—to provide clear, real-time answers. Whether a shopper clicks on a frequently asked question or types in their own, the AI assistant can respond immediately with context-rich insights, helping them feel confident in their choices.

At the core of Trail’s pilot were two complementary AI agents—the Product Q&A Agent and the Compare Agent—each targeting a distinct pain point in the buying process.

• The Product Q&A Agent delivers instant, trustworthy answers directly on the product page, pulling from product data, manuals, and internal content to give shoppers clear, real-time responses to their questions. No more digging through FAQs or fine print.
• The Compare Agent transforms dense spec tables into plain-language insights, showing shoppers the key differences between models in seconds and giving them the clarity and confidence to make faster, smarter decisions. Both agents were embedded through lightweight JavaScript integrations—safe to test within Trail’s existing Magento site, but built to carry forward seamlessly to an upcoming commercetools relaunch. This “build once, use twice” strategy ensured Trail could innovate now without creating technical debt later.

Behind the scenes, the system leveraged LangGraph from LangChain, orchestrating an advanced multi-agent architecture that balanced speed, context, and reliability. Background agents processed data in parallel, context engineering ensured relevance and tone alignment, and runtime model-swapping kept performance sharp. Fine-tuned evaluations and automated guardrails not only maintain accuracy and safety—ensuring the AI-powered agents stay accurate, on-brand, and compliant—they also monitor performance so it improves continuously, making the agents a trustworthy extension of the retailer’s brand.

Want a peek under the hood? See how Orium’s engineering team made the Compare Agent 3× faster in Diagnosing Latency: Making Our Agent 3× Faster—a behind-the-scenes look at the performance breakthroughs that power next-generation shopping experiences.

Rather than rushing to deploy the agents sitewide, we took a deliberate pilot-first approach, running structured A/B tests to validate engagement, answer quality, and conversion performance across multiple product pages.

The outcome was clear: for Trail’s pilot, add-to-cart rates rose nearly 10x compared to the control group. Shoppers who engaged with the AI tools spent more time on-page, explored more products, and converted with greater confidence.

Beyond conversion, operational efficiency improved too. By answering routine product questions instantly, the AI reduced incoming support volume and freed staff to focus on more complex customer needs. And because both agents were composable by design, Trail proved it could safely integrate generative AI into its retail operations—testing, measuring, and iterating without disruption.

For ecommerce leaders, this isn’t just about AI hype, it’s about measurable impact. As Josh Johnston, Senior Director of Online Experience at Trail, put it:

“These pilots showed us how we can safely test new AI capabilities today while building a foundation that carries forward to our next-generation site.” The success of Trail’s pilot is proof that the future of retail isn’t just about adopting AI, it’s about applying it responsibly to solve real customer problems. By simplifying complexity, removing friction, and earning trust, a multi-agent AI assistant unlocks new growth opportunities while delivering an experience customers actually enjoy, transforming digital commerce into something far more human: conversational, intelligent, and built for confidence.