GPT Store: the plugin ecosystem that recommends brands without you knowing

Your customers use AI assistants specialized in your industry every single day — built by third parties on the ChatGPT store — and they ask them for advice on products and suppliers. You probably don't even know these assistants exist. Those GPTs recommend the brands that are in their sources, and ignore everyone else: it's like having a sales rep working 24/7 who only works for your competitors. Twenty minutes of analysis tells you whether you're in or out — and from there you can figure out what to do.

There are tens of thousands of vertical assistants on the GPT Store today, built by third parties, and each one decides, every day, which brands to cite when answering its users. Do they cite yours or not? Most Italian business owners have never even asked themselves the question.

This is the difference between the Custom GPT as a tool to build — which I’ve covered elsewhere in this series dedicated to AI platforms — and the Custom GPT as an ecosystem you either exist in or you don’t. Today I’ll explain the second side: the store as a distributed recommendation channel, which works even if you’ve never opened an OpenAI creator account.

The GPT Store isn’t an app store, it’s an index of vertical assistants

OpenAI launched the GPT Store in January 2024 as a public directory of Custom GPTs built by individual creators and companies. Each one has its own knowledge base, its own system prompt, and answers using those documents plus the base GPT model — plus, if browsing is enabled, sources from the open web.

From your standpoint as a brand, this means something very concrete: every Custom GPT in your industry is an independent citation channel. If a tool manufacturer from the Bergamo area doesn’t appear in the knowledge base or in the web sources pulled by the “CNC Tooling Consultant” GPT built by a Turin-based trainer with 15,000 users a month, those 15,000 professionals will hear other names recommended — not his — for months.

Let me say it upfront, as I always do: there is no academic literature on the internal recommendation mechanism of the GPT Store. Formal research on skill-based systems and agent tools — of which Custom GPTs are a commercial expression — documents adjacent principles, not this specific case. What I’m telling you here is an explicit deduction starting from the known workings of retrieval augmented generation inside ChatGPT and from the observable behavior of GPTs published on the store.

How a third-party GPT decides who to cite

What I’ve been observing since the store matured, around mid-2025, is a clear pattern — even if it isn’t scientifically measured: vertical Custom GPTs tend to recurrently cite a narrow set of brands — typically three or four — per query category. The criteria that seem to emerge are three:

• The documents the creator uploads into the knowledge base carry a lot of weight — if you’re in a PDF the creator uses as a source, you’re practically guaranteed to show up in the output
• The web sources retrieved by browsing, when it’s enabled, pull from sites already present in the base model’s training plus pages recently updated on the topic
• Consistency of the brand name across different sources matters: a manufacturer cited as “Rossi Utensili Srl” in a PDF and as “Rossi Utensili” on a web page risks being treated as two different entities and loses weight

From this follows a sharp operational point: to exist in the ecosystem you have to claim not your Custom GPT, but the information surface that third-party creators use as a source. A broader surface, harder to control, with slower returns.

The reverse engineering I did on industrial vertical GPTs

Let me tell you about a small observational exercise I ran over the past few months. I took six Custom GPTs published on the store dedicated to technical consulting on industrial tooling and CNC machining — the kind that in Italy interests workshops between Bergamo, Brescia, Reggio Emilia and Vicenza. For each one I ran ten typical commercial queries (“recommend an Italian manufacturer of solid carbide milling cutters for stainless steels”, “who supplies custom-designed tools in small batches in Lombardy”). Sixty queries in total.

Honest limitations: six GPTs are not the whole landscape, ten queries per GPT are few, and the behavior changes as creators update their knowledge bases. An indicative test, not a study. For real analysis you need professional monitoring tools on larger samples.

What emerged. Out of sixty queries, roughly 70% of the citations concentrated on eight recurring brands — a small club — and the remaining 30% was scattered across some thirty names cited only once. The club brands shared three common traits: product pages with measurable data (materials, geometries, tolerances), presence in Italian trade magazines with bylined articles, a Wikidata entry linked to the manufacturing region. The brands cited only once had good commercial websites but little third-party editorial coverage.

What it means for a tool manufacturer in the Bergamo area

Take a workshop in Bergamo that makes special custom-designed tools for CNC. Its customers are shop-floor supervisors and procurement managers who, more and more, open ChatGPT and ask “who do you recommend for X in Northern Italy”. If that same supervisor also opens a vertical Custom GPT like “CNC Tooling Consultant for small batches”, they’re consulting a system that pulls from sources chosen by someone else, not from base ChatGPT. And in that system, the workshop’s brand is either there or it isn’t. The visibility it had built on Google doesn’t count: that surface is a different planet.

The operational consequence is that visibility in the Custom GPT ecosystem isn’t built with classic SEO, and it isn’t built by publishing your own Custom GPT either. It’s built by becoming an authoritative source on the surfaces that vertical GPT creators use: online trade magazines, specialized B2B portals, Wikipedia and Wikidata, downloadable bylined technical documents. It’s the same thread I explored when talking about backlinks as a citation proxy: the source citation is the new backlink, and the GPT Store amplifies its cascading effect.

The test you can run in twenty minutes on your industry

Operational protocol. It requires no paid tools, just a ChatGPT account.

• Go to “Explore GPTs” and search three key terms from your industry. Take the first three Custom GPTs by relevance.
• For each one, run five direct commercial queries that one of your typical buyers would ask. Note which brands come up and how many times.
• Check whether your brand ever appears. If yes, in what position. If not, that’s your first red flag.
• For the three most-cited brands, look at: do they have spec sheets with concrete data? A visible author byline? A Wikidata entry?
• Check your presence in Google’s Rich Results Test: if “Organization” doesn’t show up, it’s one of the first gaps to close.

Decision threshold, over fifteen queries spread across three vertical GPTs:

• 0 citations → you’re outside the ecosystem, a structural problem
• 1-3 → weak anchoring, not reproducible
• 4 or more → you’re in the loop, work to consolidate

It’s an entry-level check. Systematic analysis requires professional AI brand tracking tools that monitor the same set of Custom GPTs over time.

The mistakes I see most often

• Confusing “publishing your own Custom GPT” with “being visible in the store”. They are two different things. Publishing gives you a direct channel with people already looking for you. Being visible in the ecosystem means appearing inside other people’s GPTs: a game played on the sources, not on the store.
• Building a self-referential Custom GPT with no distribution. A GPT published without a distribution plan is a website with zero traffic. The store doesn’t have a discovery algorithm comparable to Google’s.
• Ignoring the Wikidata surface. For vertical industrial sectors, the Wikidata entry linked to the manufacturing region is one of the signals most recognized by third-party GPTs that import structured data. I covered this in the piece on the Google Knowledge Graph entry.
• Not measuring the usage volume of the vertical GPTs in your industry. The number of conversations on the listing gives you an idea. A GPT with 10 uses carries zero weight. One with 50,000 is a channel worth claiming.

What to actually do this quarter

For a tool manufacturer in the Bergamo area, for an Abruzzo wine e-commerce, for a notary office in Lecce — for any Italian SME that wants to exist in the Custom GPT ecosystem, these are the top actions for the quarter.

• Map the top ten Custom GPTs in your industry on the store, assess their visible usage volume, and run ten commercial queries on each. Build a “GPT × cited brands” table to measure your starting position.
• Identify the five third-party sources that recur most in the answers (trade magazines, B2B portals, Wikipedia). Work to be present there with bylined, citable content.
• Update or create your Wikidata entry with explicit links to the region, the sector and certifications. Brand name consistency across all sources, zero variations.
• Publish 4-6 deep technical spec sheets on your flagship products on your website, with measurable data and a visible author byline. These are the pages GPT creators pull with browsing enabled.
• Re-run the test every 60 days and track the trend. AI visibility mechanisms are slow to build.

If you want the complementary side — how to build your own Custom GPT and make it the default source in your industry — I covered it in GPT Store and Custom GPT: how to become the default source.

The thread that holds it all together

The GPT Store is a textbook case of a principle that recurs throughout this series: visibility in AI answers is never played on a single channel, it’s played on the information surfaces that the various channels choose as sources. In the case of the store, OpenAI doesn’t control those surfaces — the creators of the individual Custom GPTs do. Hundreds of human editors selecting sources and uploading knowledge bases. Your brand is either on their radar or it isn’t.

The real game, in 2026, is becoming an editorial reference on the vertical topics of your industry. No longer just on your own website, no longer just on Google — also in the distributed ecosystem of AI assistants that are quietly replacing information searches for entire categories of professional buyers.