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Revenue
$1 Billion+

Attributed Revenue

Growth
20+

Industry Verticals

Conversion
Up to 50X

ROAS Delivered

We are Trusted By
Sherwin Williams
Mettler Toledo
Imperial Sugar
MIR
Maxon
USDA
Sherwin Williams
Mettler Toledo
Imperial Sugar
MIR
Maxon
USDA
What AI Agents Get Wrong About Your Products
The Accuracy Problem

Being Wrong Costs More Than Being Missing

Most teams preparing for AI search are asking whether they show up at all. That is the easier problem. The harder one is what gets said when they do.

Agents do not fail loudly. When product data is thin, stale, or scattered across distributor pages and marketplace listings, they answer anyway, confidently, using whatever they can reconstruct. A missing compatibility field does not produce a blank. It produces a recommendation for someone else.

This is a tactical session, not a trends talk. We show how agents assemble a product answer, which of your fields they actually read, and the order to fix them in. You leave able to test your own catalog the same afternoon.

Buyer Adoption
43 %

Of US online shoppers used an AI assistant to research a product in the previous 90 days. Your catalog is already being described to buyers, whether or not you have prepared it.

Answer Accuracy
71.8 %

Of 2,500 evaluated interaction steps failed to achieve complete product accuracy across identity, attributes and compliance. Fluent, confident answers that diverge from the truth are the norm, not the exception.

Availability
52.7 %

Of availability lookups failed, meaning over half of AI-recommended products could not be reliably purchased as described. Variant confusion was the single most common failure mode.

About The Webinar

What You’ll Learn

A 60-minute working session with two CommerceShop leaders. We start with how an agent builds a product answer, then work through the fields that break it and the order to repair them.

01 / 06
01
How Agents Assemble a Product Answer

The sources an agent pulls from, the order it trusts them in, and why a distributor listing often outranks your own product page as the version it believes.

02
Why Strong SEO Still Produces Wrong Answers

Ranking and citation are different problems with different inputs. What a well-optimized page still fails to give an agent, and why your analytics never flags it.

03
The Five Fields That Break Recommendations

The specific product data fields that most often cause an agent to misstate, omit, or recommend around you. What each one should contain and where it usually goes wrong.

04
How to Test Your Own Top SKUs

The prompts to run against ChatGPT, Gemini, Claude and Perplexity, how to read what comes back, and how to trace an error to the page that caused it.

05
Live Catalog Teardowns

Real stores queried live, with the errors found and the fix identified. Shopify, Adobe Commerce, WooCommerce and BigCommerce examples.

06
Your Fix Order

How to rank the errors by revenue exposure rather than effort, who owns each fix, and what to hand your development team first.

WHO SHOULD ATTEND

Built for Teams Whose Products Are Being Described by Someone Else

01

Ecommerce Directors and Heads of Digital

See where your catalog is being misrepresented and what it is costing before it shows up as a soft quarter.

02

Marketing Leaders and Growth Teams

Understand why AI visibility work stalls when the underlying product data was never the priority.

03

Product Data and PIM Owners

Field-level detail on what agents actually read, so you can prioritize enrichment work by revenue impact.

04

SEO, Content and Merchandising Teams

Where ranking work ends and accuracy work begins, and how to test the difference on your own SKUs.

05

Manufacturers and Distributors

Spec-driven catalogs break agent answers more often than any other category. Built with your data model in mind.

06

Parts Retailers and Compatibility-Led Catalogs

When fit and compatibility decide the sale, a missing attribute is the difference between the shortlist and silence.

EXCLUSIVE WEBINAR OFFER

Free AI Product Accuracy Report for Your Store

Select yes when you register and our team manually audits your catalog. No tool, no generic readiness score. We query the major engines about your products and document every error we find, using the same method we run on client stores.

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MEET THE SPEAKERS

Two Perspectives on Agent-Ready Product Data

Sathish Kumar Mariappan

Sathish Kumar Mariappan

CEO, CommerceShop

Sathish works with retailers, manufacturers and DTC brands on the data and platform foundations that decide how products surface in AI-driven discovery. He separates what genuinely changes in the stack from what is still noise.

Ed Keibler

Ed Keibler

VP – Ecommerce, CommerceShop

Ed works with ecommerce teams across Shopify, BigCommerce, Adobe Commerce and WooCommerce. He turns catalog and product data problems into fix lists their teams can actually ship, with the scar tissue from running it across 20+ verticals.

Reserve Your Spot

Join us live on August 27 and leave knowing what the major engines say about your products, and which fix is worth making first.

Register Now

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