Michael McBride
Founder & CEO, Atelier Commerce
Michael McBride
Founder & CEO, Atelier Commerce
Search is split into two jobs: rank the page, and feed the answer. We enriched Moonglow's product data once and made it work for both.
Search is split into two jobs: rank the page, and feed the answer. We enriched Moonglow's product data once and made it work for both.
The Challenge
Moonglow had years of catalog history and a loyal audience, but the product data underneath was working against discoverability.
Sprawling, inconsistent catalog. Generic and duplicative titles, thin descriptions, and collection structures that did not map to how people actually search. Weak technical SEO foundation. Inconsistent or missing structured data meant the catalog rarely qualified for rich results, and left opportunity on the table in image and shopping surfaces. No AEO posture at all. AI Overviews and answer engines were already pulling moon-phase and lunar-personality questions out of the SERP. Without enriched, well-structured content, the brand risked being summarized by competitors rather than cited itself. Search itself was changing. A growing share of queries now resolve inside an AI Overview before a user ever clicks. The brief: clean up the SEO foundation, enrich the product data as a merchandising exercise, and structure everything so Moonglow wins in both classic search and answer engines.
The Challenge
Moonglow had years of catalog history and a loyal audience, but the product data underneath was working against discoverability.
Sprawling, inconsistent catalog. Generic and duplicative titles, thin descriptions, and collection structures that did not map to how people actually search. Weak technical SEO foundation. Inconsistent or missing structured data meant the catalog rarely qualified for rich results, and left opportunity on the table in image and shopping surfaces. No AEO posture at all. AI Overviews and answer engines were already pulling moon-phase and lunar-personality questions out of the SERP. Without enriched, well-structured content, the brand risked being summarized by competitors rather than cited itself. Search itself was changing. A growing share of queries now resolve inside an AI Overview before a user ever clicks. The brief: clean up the SEO foundation, enrich the product data as a merchandising exercise, and structure everything so Moonglow wins in both classic search and answer engines.
The System
Two offerings, one enriched data layer.
Strategic merchandising and product data enrichment. We rebuilt the product data as a merchandising asset: descriptive, search-aligned titles (material plus style), richer descriptions, consistent attributes (metal, style, occasion), and curated cross-aisle collections. Better data merchandises better and ranks better at the same time. Sitewide SEO cleanup. We standardized titles and metadata, fixed heading hierarchy, tightened internal linking and breadcrumbs, mapped collections to real search demand, and resolved the technical debt that had been suppressing rich-result eligibility across the catalog. Structured data and AI discoverability (AEO). We deployed a full structured-data stack (Product, Offer, AggregateRating, BreadcrumbList, Organization) so the catalog qualifies for rich results and is machine-readable for AI answer engines. We also leaned into the brand's editorial library so it is the cited source on the informational questions that now trigger AI Overviews. Built for both jobs at once. The same enriched data feeds the classic listing, the rich result, and the AI Overview. One investment, three surfaces.
The System
Two offerings, one enriched data layer.
Strategic merchandising and product data enrichment. We rebuilt the product data as a merchandising asset: descriptive, search-aligned titles (material plus style), richer descriptions, consistent attributes (metal, style, occasion), and curated cross-aisle collections. Better data merchandises better and ranks better at the same time. Sitewide SEO cleanup. We standardized titles and metadata, fixed heading hierarchy, tightened internal linking and breadcrumbs, mapped collections to real search demand, and resolved the technical debt that had been suppressing rich-result eligibility across the catalog. Structured data and AI discoverability (AEO). We deployed a full structured-data stack (Product, Offer, AggregateRating, BreadcrumbList, Organization) so the catalog qualifies for rich results and is machine-readable for AI answer engines. We also leaned into the brand's editorial library so it is the cited source on the informational questions that now trigger AI Overviews. Built for both jobs at once. The same enriched data feeds the classic listing, the rich result, and the AI Overview. One investment, three surfaces.
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UTV Source competes in one of the most crowded categories online. Thousands of verified reviews. A perfect 5.0 storefront. A rewards program that pushed review collection up 80 percent since 2024. Not luck. Trust, engineered, by the thousands.
Latest projects
Alicia Adams Alpaca: A Hundred-plus Colorways For Every Season, Every Channel, and Not A Single Studio Booked
Alicia Adams Alpaca crafts heirloom throws and home pieces earning its place in Architectural Digest, Vogue, and Veranda. Lifestyle photography is the industry's costliest bottleneck. So we built the room instead of renting it. Photorealistic scenes, art-directed like editorial photography. Every color, every season, at a fraction of the cost.
UTV Source: The reviews, rewards, and trust that win a brutally competitive category
UTV Source competes in one of the most crowded categories online. Thousands of verified reviews. A perfect 5.0 storefront. A rewards program that pushed review collection up 80 percent since 2024. Not luck. Trust, engineered, by the thousands.
