Case Study: From Invisible Catalogue to AI Cited Authority

Introduction

Most B2B foodservice businesses live or die on existing relationships. Search is rarely the channel that wins them new customers, so when it does start delivering, the impact compounds quickly. This case study covers how a South Australian foodservice distributor with a four thousand product catalogue moved from an almost entirely branded search footprint to being cited in Google AI Overviews, ChatGPT, and Perplexity, and ranking top three for the highest intent local B2B queries in their market.

Client Background

The client is an Australian foodservice distributor supplying restaurants, schools, hospitality groups, and institutional kitchens. The website hosts thousands of product SKUs across categories spanning beverages, dairy, dry goods, fresh, frozen, and specialty imports. The business is well known to existing customers and rarely competes on Google for branded queries.

The challenge was that almost nobody else could find the site. New customers searching for specific products, wholesale terms, or local distribution were ending up on competitor pages because the catalogue was effectively invisible to anyone outside the existing customer base.

The Challenge

Three problems were interconnected. First, organic traffic was overwhelmingly branded, meaning growth was capped at how many people already knew the company. Second, the four thousand product catalogue was missing the basic on-page elements needed for search engines to understand what the pages were about. Titles, descriptions, taxonomy and category copy were inconsistent or missing entirely. Most product pages were uncrawled or unindexed.

Third, all site implementation routed through an external development partner, which meant every change had a queue, every deployment needed QA, and the speed of execution was set by someone outside the engagement.

Strategy

The strategy was unglamorous on purpose. Build the catalogue at scale. Fix the foundations. Let the work compound. There was no AI specific tactic. The bet was that the same on-page fundamentals that have always built search rankings would also drive AI Overview, ChatGPT, and Perplexity citations once the surface area was strong enough.

1. Catalogue at scale: Generated SEO optimised titles and descriptions across the entire product range. More than 800 product meta updates over a four month sprint, plus replacements for missing product copy.
2. Taxonomy and category copy: Wrote category and taxonomy descriptions, fixed duplicate titles in bulk, optimised the home page H1, and cleaned up missing on-page elements across the site.
3. Indexing and technical foundations: Submitted the sitemap to Google and Bing, audited redirects, resolved a legacy NZ subdomain bleeding equity, and worked with the development partner to address Core Web Vitals.
4. Internal linking and entity structure: Built internal linking between new product and category templates, strengthened entity signals around brand and location, and improved crawl paths.
5. Tracking and Google Business Profile: Configured GA4 key events for product clicks, enquiries, online ordering and form submissions. Optimised the Google Business Profile and refreshed photography.

SEO Implementation Highlights

Catalogue scale on-page work generated 13.4x growth in daily search impressions and 3.8x growth in daily organic clicks.

The site moved from average search position 25 to position 6, an eighteen position lift that took the brand from page three to top of page one.

Non-branded impression share grew from a near fully branded base to 80% of all impressions, meaning the site is now reaching customers who have never heard of the brand.

AI surface area emerged as a lagging indicator of the SEO work. From a near zero base, the site is now cited in 81 Google AI Overviews, by ChatGPT 84 times, and by Perplexity 18 times.

Conclusion

This case study underscores the transformative potential of on-page SEO when applied thoroughly across a deep B2B catalogue. The non-obvious finding is that AI engines reward the same fundamentals Google has always rewarded. Catalogue depth, structured taxonomy, clean metadata, indexable categories. There is no separate AI SEO playbook to chase. There is the SEO playbook, run thoroughly, with a four to six month lag before AI Overviews and ChatGPT start citing the work.

The commercial implication is that B2B businesses with deep catalogues have an outsized opportunity. Most have never invested in product level on-page work because the per-page effort felt disproportionate to per-page reward. AI search changes that calculation. The compounding return on a properly structured catalogue is now substantially higher than it was twelve months ago.

The Future Of SEO

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