One assistant replaced search and support across three business units, raising click-through 14%.
Trail Appliances Storefront Assistant
Add to cart rose nearly tenfold among engaged shoppers, measured against a control group.
Two purpose-built agents, one answering product questions and one translating specification tables into guidance.
What the agents do
One agent answers product questions from manuals and internal sources, another turns dense specification tables into plain-language guidance.
- Brand
- Trail Appliances
- Contributed by
- Orium (Systems integrator)
- Industry
- Retail, Manufacturing & Industrial
- Audience
- B2C
01
The problem
Why this exists
Appliances are compared on specifications that only mean something to someone who already knows the category. A shopper looking at two dishwashers sees two tables of numbers and no way to tell which one suits their kitchen.
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The answers exist, in the manual, in the product data, and in the knowledge a salesperson has. None of them are on the page at the moment the shopper is deciding, and routing to a pre-defined FAQ answers a different question from the one being asked.
02
Outcomes
What changes when this is in place
- Add-to-cart rates rose nearly tenfold among engaged shoppers, validated against a control group rather than measured against a baseline.
- Shoppers get answers synthesized in real time from product data, manuals, and internal knowledge bases, rather than being routed to a pre-written response.
- Specification comparison returns plain-language decision guidance instead of a second table.
03
How it works
Architecture
The assistant is two purpose-built agents rather than one general one, which is the design decision worth noting. The Product Q&A Agent retrieves and synthesizes answers from product data, manuals, and internal knowledge bases in real time. The Compare Agent interprets shopper intent and translates dense specification tables into decision guidance.
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Splitting them means each has a bounded job and a bounded source set, so the comparison agent is not answering warranty questions and the question agent is not inventing a comparison.
Orium describes the distinction from automation and predictive AI directly: the agents reason over unstructured queries, interpret context dynamically, and generate novel responses, rather than routing to pre-defined answers or predicting from historical patterns.
Open standards used
REST APIs
Tech stack
Product Q&A AgentCompare AgentProduct data and manualsInternal knowledge bases
Resources
Next step
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Everyone credited on this solution
- OriumSystems integrator
Every organization named here has a page listing the rest of its work on the Hub.
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Verification
Published case study
The detail on this page is drawn from a case study published by the contributor or the MACH Alliance. Any figures on this page are the ones that case study states.
Where this came from
Solution detail is published from the contributor's 2026 Agent Ready submission to the MACH Alliance. Submitter names and contact details are not published.
Status: Live in productionAdded Jul 8, 2026A listing is an editorial decision, not a MACH Alliance certification.
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