In your industry there are already AI chatbots built specifically for the people who buy what you sell — and your buyers use them every day to do research, compare suppliers and build short-lists. If you are not in the database of these niche platforms, you do not exist at the very moment the purchasing decision takes shape. Getting into those datasets is often easier than it seems, but it requires knowing where to look.
Microsoft Copilot is not “just” Bing with GPT-4. It is an ecosystem that includes Windows, Office, Edge, Teams. If you are in the Bing ecosystem, you are everywhere Microsoft is.
But there is a layer above it. And it is becoming the new playing field for those who sell in specialist sectors: vertical AI chatbots. Systems trained on proprietary knowledge bases for a single industry — legal, medical, financial, manufacturing — that you never see if you search on ChatGPT, because they are not generalist chatbots. But your buyers use them every day.
Let me explain why this changes the rules for anyone doing business in your niche, especially if you sell in industrial B2B and your buyer is not the average ChatGPT user.
What a vertical AI chatbot is and why it does not look like the others
A generalist chatbot (ChatGPT, Gemini, Claude) is trained on the open web plus a set of licensed sources. It covers everything, superficially. A vertical chatbot is the opposite: it covers ONE domain, in depth, with data that is not on the open web.
Think of Harvey for law firms, Ada Health for medical triage, DoNotPay for consumer bureaucratic procedures, BloombergGPT for finance. Each of these has a proprietary knowledge base: court rulings, anonymized medical records, SEC filings, niche papers, technical manuals. Stuff that a generalist LLM has never digested, because it is not sitting on a public blog.
In the world of research on retrieval systems, the principle has been established for years: a model is only as good as the corpus it was trained on, or as good as the index it connects to at answer time. I talked about this in “How AI engines think and the role of tokenization” and in the piece on backlinks as a citation proxy. From this principle follows a stark consequence: if your brand is inside that vertical corpus, the AI cites you as a natural source. If you are not there, you are invisible even when the buyer searches for exactly what you sell.
This is not a provocative claim, it is a direct deduction: a model cannot cite what it has never seen.
Why your SME should care (even if you are not in legal or medical)
Let me give you a concrete example, not a generalist one. In Terni there are several companies that produce special stainless steels and high-performance metallurgical alloys — aerospace, valves, chemical plants. Their buyer is not someone who opens ChatGPT and asks “best stainless steel”. It is a technical buyer who is choosing among three European suppliers for a 2 million euro order.
That buyer, today, queries three systems in parallel:
- a generalist chatbot to get oriented (Copilot, ChatGPT)
- a vertical-aware AI engine (Perplexity with academic filters)
- a sector chatbot internal to their company or to a B2B marketplace (MatWeb for materials, Thomasnet for US suppliers, Europages AI for Europe)
The third one is what decides. And it is the only one on which you have no visibility unless you move proactively.
This is the point of the article: showing up in AI answers does not just mean ranking on ChatGPT. It means being in the dataset of the right chatbot for your buyer. For an Umbrian steel company specialized in austenitic steels, appearing on Copilot is a bonus. Appearing on an industrial B2B vertical chatbot is commercial survival.
Confusing “being indexed by Google” with “being in the vertical chatbot’s dataset”.
The multi-AI test I ran on 15 industrial queries
I took 15 realistic queries that an industrial buyer might type, focused on the world of special stainless steels — profiles like those of the Terni companies. Then I ran them across three systems:
- Microsoft Copilot (generalist with Bing grounding)
- ChatGPT (without browsing, then with browsing)
- Perplexity (Pro mode, default sources)
The queries were of the type: “European suppliers of duplex stainless steel for chemical valves”, “comparison of austenitic steels AISI 316L vs 904L for chloride environments”, “Italian specialty steel producers for aerospace”, “nickel-based alloys for high temperatures EU suppliers”, and so on across 15 variants.
What I saw on this sample — an indicative test, not a study, the sample is small and focused on a single sector:
- Copilot cited on average 2-3 brands per query, almost always the global big names (ThyssenKrupp, Outokumpu, Aperam). Specialized Italian SMEs appeared in 2 queries out of 15.
- ChatGPT with browsing produced patterns similar to Copilot: the same big names, plus a couple of B2B directories (Kompass, Europages).
- Perplexity was more varied: in more than 40% of the queries it cited at least one niche source (technical papers, whitepapers from mid-size producers, MatWeb pages). But the Terni SMEs still appeared rarely: 3 times out of 15.
The figure that matters is another one. When I explicitly asked “special stainless steel producers in Umbria” or “Terni steelmaking aerospace”, the results were inconsistent: Copilot invented or mixed things up; Perplexity cited 2-3 local sources but often old ones (press releases from 2018); ChatGPT pulled from Wikipedia, which on this segment is skeletal.
The operational consequence: if you are a Terni company in specialty steelmaking and you rely only on Google + your corporate website, the generalist AI barely sees you. To get into the B2B vertical chatbots (where the buyer actually decides) you have to work on a different channel from classic SEO.
Open the Google Rich Results Test, paste the URL of one of your product pages, and look for “Product” and “Organization” in the results.
How to identify the vertical chatbots in your sector
There is no single list. Every sector has its own. But the pattern for finding them is repeatable:
- Ask 3 of your current customers “when you need to find a new supplier or compare technical specs, what digital tool do you use besides Google?”. In 60-70% of cases you will get the names of sector B2B platforms with an AI component.
- Search on Google for “AI chatbot” + the name of your sector in English (“specialty steel”, “industrial valves”, “metal alloys”). The platforms that have integrated an LLM into their knowledge base will surface.
- Look at the vertical B2B marketplaces (MatWeb, Thomasnet, Europages, Kompass) and check whether they have an “AI assistant” or “smart search” feature. If they do, that is already a vertical chatbot for all intents and purposes.
- If you sell in a regulated sector (aerospace, medical, food contact), check whether there are certification databases with AI access: large buying companies often use them for prequalification.
For your entry-level audit this is enough. The real analysis — full mapping of the vertical chatbots, inclusion priorities, partnership plan — requires professional tools and dedicated time.
The mistakes I see most often
Confusing “being indexed by Google” with “being in the vertical chatbot’s dataset”. They are two separate universes. A B2B vertical chatbot does not read Google: it reads its internal dataset, which is updated through manual processes or APIs.
Waiting for the providers to contact you. It does not happen. Vertical B2B platforms have sales teams, but they do not actively scout Italian SMEs: the SMEs have to apply. If you are a steel company in Terni with 80 employees, you are not on their radar until you introduce yourself.
Neglecting the machine-readable data sheet. Even if you get into the dataset, if your product sheet is a scanned PDF the chatbot will not digest it. You need a structured sheet, Product/Organization schema markup, recognizable entities. I talked about this in “Named entity recognition and why it matters for AI“.
Thinking that “if I’m on Perplexity that’s enough”. Perplexity is generalist with open sources. A technical buyer who has to sign off on a 2 million euro order does not trust a Perplexity answer: they move to the vertical chatbot of their B2B platform. You have to be on both.
What to do concretely this week
- Make the phone call to the 3 customers to map the vertical chatbots they actually use.
- Open the Google Rich Results Test, paste the URL of one of your product pages, and look for “Product” and “Organization” in the results. If they do not appear, your sheet is not readable by any chatbot — generalist or vertical.
- Check on Wikidata whether your company has an entity record. If it is not there, that is already a sign of structural invisibility. Read author entity recognition to understand why.
- Contact 1-2 vertical chatbot providers in your sector (B2B marketplaces with an AI assistant) and ask “how do I get into your dataset?”. It is often a commercial partnership, sometimes a self-submission form, sometimes API integration.
No forced urgency: the market for vertical chatbots is still being built, you have time. But whoever moves now will find themselves, 18 months from now, with an advantage that will be costly for latecomers to close.
The point, again
Visibility in AI answers is not a single game you win on ChatGPT. It is a series of parallel games, one per type of chatbot, one per dataset. Generalist chatbots matter. The vertical chatbots in your sector matter more, for your bottom line, because that is where your buyers decide.
In the next articles in this series I will talk about how enterprise AI platforms manage brand inclusion in their datasets, and about what strategically differentiates Copilot from ChatGPT for an Italian B2B business. If you have not yet read the pieces on E-E-A-T for AI and on Google Knowledge Graph, go back to them: they are the floor on which everything else rests.
