EXON eCommerce aggregator

AI SEO model across 60 sites and 3M pages.

2020-2022 - eCommerce aggregator. Role: Head of SEO at Moonshot Marketing. Text-only career context, not an SEOH client case or endorsement.

Verified sites
60 EXON eCommerce aggregator sites
Verified scale
3M pages
Role period
2020-2022
01

Context

At Moonshot Marketing, Menashe worked with Exon Media and implemented an AI SEO model across 60 EXON eCommerce aggregator sites with 3M pages.

02

The challenge

Aggregator SEO at this scale needs template discipline, entity consistency, crawl control, and a way to apply AI-supported decisions without flooding the index with weak pages.

03

What I did

  • Reviewed large-site patterns with Screaming Frog, SEMrush, Ahrefs, GA4, Google Search Console, and manual roadmap review.
  • Implemented an AI SEO model across 60 EXON eCommerce aggregator sites.
  • Mapped decisions across a 3M-page environment where templates carried most of the risk.
  • Prioritized scalable structure, crawl hygiene, and page usefulness.
  • Connected AI-supported SEO work to eCommerce aggregator constraints.
04

Results

  • The AI SEO model covered 60 EXON eCommerce aggregator sites.
  • The implementation environment included 3M pages.
  • The work became a major AI-era large-site SEO reference point in Menashe's career.

Founder reflection

What this taught me.

AI support is useful only when the operating rules are clear. At millions of pages, weak rules create weak pages very quickly.

What this means for SEOH

How it translates now.

SEOH applies this to eCommerce and AI-search work by reviewing templates, schema, crawl control, and AI-ready content before scaling production.

Recommended next step

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