AI Platforms

ChatGPT, Perplexity, Gemini and Claude don't draw from the same sources and don't follow the same logic — optimizing for just one means being invisible on the other three. If your clients use different platforms, you're reaching only a fraction of them without knowing it. Knowing the specific rules of each AI engine lets you cover all four channels and multiply the occasions in which your name appears in the right place.

Let me tell you about a case I saw two weeks ago, because it’s the most precise snapshot of how most Italian entrepreneurs get their approach to AI visibility wrong. A real estate agency in Lecce, specialized in historic villas in Salento, comes to me convinced it’s “visible on AI.” They were half right. We open Perplexity, type “real estate agencies for historic villas in Salento”: it appears in the second answer, cited with a precise anchor and a source linked to their site. Everything perfect. We open ChatGPT, same query: silence, no mention, a competitor from Bari wins — one that, in terms of web positioning, is clearly below them. We open Gemini: it’s cited together with a consortium of Apulian agencies, in a confused way, with no direct link. We open Claude: the model politely declines to make specific commercial recommendations and speaks in general categories.

Four platforms, four completely different results on the same query. And the owner, until that moment, was living inside an illusion: he had checked Perplexity, seen his name, and concluded “we’re all set.” He didn’t know that this test, on its own, was hiding 80% of the problem from him.

This is the thread that holds together all 40 articles I’ve written on this topic. AI platforms are not interchangeable. They have different architectures, different sources, different criteria, different users. Thinking of “AI visibility” as a single field is the same mistake people made ten years ago when they believed that optimizing for Google was the same as optimizing for Bing — wrong already back then, devastating today now that generative platforms are five, six, soon ten, and each one draws from its own universe of sources.

Those who blur the games lose on all of them. Those who separate them, and design differentiated strategies for each, start to see their name appear where competitors don’t reach. Here I give you the complete map, divided into five blocks — one for each platform family — plus a final operational block to build your visibility matrix across all of them.

The era of parallel AIs

To understand why you need a different strategy for each platform you have to accept a fact that many haven’t yet internalized. Generative AIs are not graphic variants of the same engine. They are different products, built by different companies, with different training corpora, with different document retrieval (RAG) systems, with different citation criteria and with different user bases.

ChatGPT, today, has hundreds of millions of weekly users and for many it has become the starting point for any knowledge query. Its behavior mixes the model’s internal knowledge with live browsing and third-party plugins. The answers are often syntheses without explicit sources, unless browse mode is active or the query pushes toward citations.

Perplexity has chosen the opposite path: every answer is anchored to web sources, cited inline with numbers, with an almost obsessive focus on traceability. It has become the preferred engine for professional, research and B2B purchasing queries. Its user base is smaller than ChatGPT’s but highly qualified.

Google’s Gemini lives inside the Mountain View ecosystem — Search with AI Overview, Workspace, Vertex for businesses, Android. Its sources draw massively from the Google index, from Merchant products, from images, from YouTube videos. It’s the engine that most resembles a “Google with superpowers,” for better and for worse.

Anthropic’s Claude has yet another identity: less used for mass knowledge search, much used in professional and enterprise contexts, with a huge context window that makes it the reference model for analyzing long documents. Its commercial recommendations are more cautious, its citations more filtered.

And then there’s the long tail: Bing/Copilot integrated across all of Microsoft, the vertical AIs of marketplaces (Amazon Rufus, Booking AI, eBay), the AIs in social networks (Instagram, TikTok), voice assistants (Alexa, Google Home, Siri), the meta-aggregators. Each with its own game.

It means that the query “best supplier of electromechanical components for the pharmaceutical industry” can produce completely different answers depending on where you launch it. It means that one of your potential clients may open two or three of them before deciding who to contact, and you need to be in all of them. It means that the GEO strategy is not one, it’s five families of connected but differentiated strategies.

In the articles on how AI engines think, on authority and credibility for AI, on AI-ready content structure, on entities and knowledge graphs, and on digital PR and citation signals, I’ve built the cross-cutting layers — the ones valid for all platforms. Here we enter the sixth layer: how all that work translates onto each specific engine, because the shared rules aren’t enough. You need platform tactics.

The map: the five worlds of AI platforms

1. ChatGPT and the OpenAI ecosystem

The first block is dedicated to ChatGPT, because it’s the engine with the largest user base and the one that, statistically, is the first place where one of your prospects looks for you when they want a recommendation. Yet it’s also the most opaque: it often doesn’t cite sources, builds synthetic answers from its internal knowledge, and changes behavior depending on whether browse mode is active or not.

The starting point is understanding how ChatGPT builds an answer — what weights it assigns to internal knowledge, to browsing, to reasoning. Without this, every optimization attempt is guesswork. In the article “ChatGPT Response Architecture: how the model assembles the answer between training and browse mode” I explain the flow diagram I use to reason with clients.

Browse mode deserves a chapter of its own. When ChatGPT decides to go on the web — and it doesn’t always do so — the criteria with which it chooses sources are different from those with which it draws from its memory. Understanding when it activates and what it rewards lets you get picked up predictably. In the deep dive “ChatGPT Browse Mode: when it activates, what it searches, how to get found” I show you the patterns.

The GPT Store and Custom GPTs are a surface underused by those doing GEO in Italy. Having a vertical Custom GPT for your sector, well built, positions you as a reference point for users looking for specificity. I talk about it in GPT Store and Custom GPT: how to build your Custom GPT as an AI visibility asset.

ChatGPT’s personalized memory changes the rules of the game also for those who create content. When a user “teaches” the model their preferences, your brand can enter that set of preferences and be recalled even out of context. In the article “ChatGPT Memory Personalization: how your brand can enter users’ persistent memory I clarify how it works and what you can do.

If you sell B2B, ChatGPT Enterprise and Team are the channel where decision-makers spend hours a day. Companies adopt it as a daily work tool, and your brand needs to be present in the answers that come out in that context. The deep dive on ChatGPT Enterprise and Business: how to be visible in pro users’ answers explains the specifics.

A metric few measure is ChatGPT’s referral pattern to your site: when the model cites a source and the user clicks, traffic with specific characteristics arrives. Knowing these patterns helps measure the real impact. I talk about it in ChatGPT Referral Pattern: how to read the visits ChatGPT sends to your site.

When ChatGPT cites a source, the way it cites it has its own rules — text, link, context, attribution. Understanding them lets you write content that has a higher probability of being cited. In the article on ChatGPT Citation Style: how ChatGPT formats citations and what to optimize to be chosen I describe the patterns.

The question to ask yourself: today, can you tell me whether ChatGPT cites you, in which contexts, with what frequency, in which mode (browse or internal memory)? If the answer is “I don’t know,” on ChatGPT you’re blind — and it’s probably the platform through which the largest share of your prospects passes.

2. Google Gemini and SGE

The second block is Google. For years SEO was “optimization for Google” and nothing else. Today Google has become an ecosystem within the ecosystem: it has its classic search engine, the AI Overview that drastically changes the results, Gemini as a standalone app, integrated Workspace, Vertex for businesses, Merchant for commerce. Each of these surfaces has its own logic, and they all draw from the Google index but with different criteria.

The AI Overview, formerly SGE, is probably the most impactful transformation of the SERP in the last fifteen years. It profoundly changes what appears at the top, how much traffic reaches the site, which sources get cited. Understanding its architecture is today more important than understanding the classic ranking algorithm. In the article Google SGE / AI Overview Architecture: how Google builds the AI Overview and what really matters I break down the flow.

When the AI Overview selects snippets to cite, the criteria are not identical to those of organic ranking. Understanding which content gets the AI snippet and which doesn’t has become a craft of its own. The deep dive on Google SGE Snippet Selection: what makes your snippet get chosen over the competitor’s traces the map.

Google Perspective, the feature that shows opinions and discussions inside the SERP, is a parallel channel where sources like Reddit, Quora, vertical forums weigh much more than usual. For many commercial queries, that’s where the game is played. I talk about it in Google Perspective and Discussion: how to be visible in the “discussions” results that Google now shows at the top.

Workspace with Gemini integrated is the daily AI touchpoint for millions of workers — Gmail, Docs, Sheets. When Gemini suggests a supplier inside an email your prospect is writing, you’re inside an experience that bypasses any SERP. The article on Google Workspace Gemini: how to be visible in the AI suggestions inside Gmail and Docs explains how it works.

For those doing e-commerce, the Merchant Center is the base of product data that Gemini uses for AI shopping answers. Without a clean, rich, well-structured feed, you’re invisible on commercial queries. In the article on Google Merchant Product Data for AI Shopping: how to structure the product feed for AI shopping answers I explain what changes compared to the classic Shopping feed.

Vertex AI Search, finally, is the enterprise layer: it’s the engine with which large companies are building their own internal RAG systems, and it indexes public web sources. Being well structured for Vertex means being picked up inside dozens of enterprise systems that search for you by name. In the deep dive on Google Vertex AI Enterprise Search: how to be visible inside corporate RAG systems built on Google Cloud I trace the path.

The question: in your strategy is there a specific plan for AI Overview, or are you still optimizing for the classic SERP and hoping it works for the new one too? If the agency sends you reports on “average” positions without distinguishing between classic organic and AI Overview, the report is three years old.

3. Perplexity

The third block is dedicated to Perplexity, and it’s one of the platforms where a small brand can flip the game against huge competitors if it builds the work well. Perplexity rewards substance, clarity of sources, specificity of content. Professional queries — from B2B to research — increasingly pass through there.

The starting point is understanding how Perplexity selects the sources it cites. It’s not Google, it’s not ChatGPT: it has its own hybrid system of crawling, ranking and RAG re-ranking. Understanding it tells you what kind of content to write and where to publish it. In the article on Perplexity Source Selection: how Perplexity decides which sources to cite in answers I describe the flow.

Perplexity’s citations have their own style: numbered, inline, with anchors. Understanding how to structure content to be cited clearly — not buried in a footnote — makes an enormous difference in the CTR it generates. The deep dive on Perplexity Citation Pattern: how to write to be cited clearly and clickably gives you the scheme.

Perplexity Pages is a new surface: AI-generated pages by users that stay public, indexed, cited by future queries. Appearing in the right Pages brings you recurring visibility. I talk about it in Perplexity Pages: how to be cited in the AI-generated pages that stay public for years.

Spaces are thematic collections curated by pro users, often by corporate teams. Being inside the right Spaces means being consulted by decision-makers who use Perplexity as a knowledge base. In the article on Perplexity Spaces and Collections: how to enter the collections curated by pro users I explain how it works.

A specificity few know about: Perplexity Pro and Free have different behaviors in source selection and in reasoning depth. Knowing how Pro behaves — used by the most qualified prospects — lets you calibrate content differently. The deep dive on Perplexity Pro vs Free Behavior: how source selection changes between pro and free users clarifies the differences.

Finally, the Focus Modes — Academic, Writing, Reddit, Youtube, Wolfram. Each one changes the pool of sources Perplexity considers. Knowing which Focus modes are relevant for your sector and optimizing to be present in those pools is a specific lever. I talk about it in Perplexity Focus Modes: which Focus modes matter for your sector and how to be visible in each.

The question: have you ever tested your purchasing queries on Perplexity with at least three different Focus Modes? If you’ve tested only “Web” you’re seeing a third of the picture.

4. Claude and Anthropic

The fourth block concerns Claude and the Anthropic ecosystem. Claude is an interesting case because, unlike ChatGPT and Gemini, it’s not a mass knowledge search engine: it’s used mostly by professionals, developers, analysts, in enterprise contexts. However, its influence in specialized B2B is enormous, and its technical characteristics — context window, citation style, ethical filter — affect the way you need to structure content to be seen.

The first node to understand is Anthropic’s Constitutional AI. Claude refuses or softens overly direct commercial recommendations, applies ethical filters on sources, rewards balanced content with stated limits. Understanding this logic lets you write content that Claude is happy to cite. In the article on Claude Constitutional AI Impact: how Anthropic’s Constitutional AI changes what Claude cites and what it doesn’t I explain the practical impact.

Claude’s huge context window — up to 200k tokens and beyond — changes the game for those who produce long, rich content. While other models have to truncate, Claude reads everything. I talk about it in Claude Context Window Advantage: why long, structured content works better on Claude than on other models.

Claude’s training data and the way Claude does retrieval have their specifics: which sources were weighted more, which temporal cutoffs, how document retrieval via API works. The deep dive on Claude Training Data and Retrieval: what Claude knows, where it draws from, how to do retrieval that includes you clarifies the picture.

Claude Artifacts is a feature that changes the way content gets used: users ask Claude to produce analyses, code, dashboards starting from specific sources. Being one of those sources generates a kind of “applied” visibility that few exploit. In the article on Claude Artifacts and Analysis: how to be the source used in the Artifacts that Claude produces I explain how to optimize.

There’s also a parallel universe of open models. Meta AI with Llama, distributed for free and adopted by hundreds of startups and corporate RAG systems, builds a silent visibility surface. Being “read” by Llama means being picked up in dozens of final products. I talk about it in Meta AI and Llama: how to optimize for the open model most used in enterprise production.

And then there’s the Microsoft ecosystem with Copilot and Bing, which runs across all of Office, Windows, Edge, GitHub. In this case we’re talking about a specific channel, with its own rules. The article on Bing Copilot and Microsoft Ecosystem: how to be visible in Copilot suggestions inside Office, Windows, GitHub clarifies the specifics.

The question: is your content designed also for models that reward length, balance, internal citations? Or is it short SEO pieces optimized for snippets, that bounce off Claude?

5. Bing/Copilot and the rest (the long tail)

The fifth block is the one most agencies ignore completely: the long tail of vertical, conversational, voice, and social AIs. Individually each of these surfaces is small. Summed together, they intercept a huge share of your client’s decision moments. And they are all, technically, AI engines with their own pool of sources.

Sector-specific vertical AI chatbots — AI assistants for healthcare, legal, real estate, finance — are proliferating. They’re often built by companies in the sector with general-purpose LLMs plus a vertical knowledge base. Being inside those knowledge bases is hyper-qualified visibility. In the article on Sector-Specific Vertical AI Chatbots: how to enter the specialized AI chatbots of your sector I explain how to map and penetrate them.

AI search inside marketplaces — Amazon Rufus, Booking AI, eBay, Etsy — is changing purchasing decisions. For a producer of Apulian extra virgin olive oil or for a farm stay in Cilento, being visible in Booking’s AI answers can be worth more than any classic SEO optimization. The deep dive on AI Search in Marketplaces: how to be visible in Amazon Rufus, Booking AI, eBay AI traces the method.

The problem of cross-platform consistency is cross-cutting: if your brand appears in different ways on the different platforms — cited well on Perplexity, cited badly on ChatGPT, ignored on Gemini — the signal that reaches the prospect is one of unreliability. Building consistency across platforms is a specific discipline. In the article on Cross-Platform Consistency: how to build consistency among the citations you receive on different platforms I describe the framework.

AI in social media — TikTok with its AI discovery engine, Instagram with AI suggestions, LinkedIn with the feed system and suggested posts — is a hybrid surface. Discovery is no longer just classic algorithm, it’s LLM-driven. The article on AI in Social Media: how to be visible in the AI feeds and discoveries of TikTok, Instagram, LinkedIn explains how it works.

AI meta-aggregators — tools that aggregate answers from multiple LLMs into a single answer, like You.com, Phind, Andi, Komo — are a small but highly qualified channel, used by power users. I talk about it in AI Aggregator and Meta Search AI: how to be visible in the aggregators that combine multiple LLMs.

And then there’s voice AI: Alexa, Google Home, Siri, each with its new-generation AI assistant. The voice query is different from the written one — longer, more conversational, more intent-driven. Optimizing for voice is a specialization. The article on Voice AI Alexa Google Home Siri: how to be the answer that voice assistants pronounce traces the method.

Finally, a forward-looking glance. Tomorrow’s AI platforms — Mistral growing, Grok expanding, autonomous agents that make purchases on behalf of users, AI runtimes inside operating systems — will be operational within 18-24 months. Building an adaptive posture today is different from chasing every novelty. In the deep dive on Future Platforms Forecasting: how to prepare for the next AI platforms without chasing them at random I give you the strategic framework.

The question: how many of the platforms I’ve listed in these five blocks have you tested methodically in the last 4 weeks? If the answer is “one or two,” your AI visibility map has huge gaps precisely in the points where your competitors are building an advantage.

Operational audit: the 5×5 matrix to map your visibility

The work across all platforms may seem unmanageable. It only becomes so if you tackle it without structure. The right structure is a 5×5 matrix: five platform families on the rows (ChatGPT/OpenAI, Gemini/Google, Perplexity, Claude/Anthropic, Bing/others), five signals on the columns (text citation, clickable link, positive context, recurring frequency, competitive positioning). Twenty-five cells. For each cell a score from 0 to 3. Here are the ten steps to fill it in today, all with free or freemium tools.

Pro tip

Use realistic prospect queries, never self-referential ones. For each platform note three data points: whether it cites you, in what position, with or without a clickable link.

1. Open ChatGPT (chat.openai.com), type 5 prospect queries from your sector — realistic queries, never self-referential. For an upholstered furniture maker in Forlì: “best Italian makers of custom sofas for contract projects.” Note: does it cite you? In what position? With a link?
2. Same thing on Perplexity (perplexity.ai), Web mode. Compare the cited sources with those of ChatGPT. Different? Surely. How many times are you among the sources?
3. Open Google and run the same queries: does the AI Overview appear? What does it cite? Compare with ChatGPT and Perplexity. Three platforms, three results: it’s already clear how fragmented you are.
4. Open Gemini (gemini.google.com), repeat the queries. Gemini has access to sources the others don’t (YouTube, Maps, Merchant). If you’re active on those surfaces, you should see differences.
5. Go to Claude (claude.ai), free version. The same 5 queries. Claude tends to give more general answers, but when it cites specific sources it’s a strong signal. Note it down.
6. Bing Copilot (bing.com/chat), repeat. Bing has its own crawl and its own index — the results often surprise.
7. Open the marketplace relevant to your sector: Amazon Rufus if you sell products, Booking AI if you’re in tourism, eBay/Etsy for craftsmanship. Run a query a buyer would make. Where are you?
8. Build your matrix: 5 platforms x 5 signals. For each cell, a score 0-3. Total out of 75. Below 30: nonexistent visibility. Between 30 and 50: present in a fragmented way. Above 50: you’re already doing well on multiple platforms.
Common mistake

Trying to act on all 25 cells of the matrix at once. You scatter your attention and close no gap. Choose the three priority actions and stop there.

9. Identify the three biggest gaps. Don’t work on 25 cells at once: choose the three priority actions. Example: “I’m absent on Perplexity Web,” “On Gemini AI Overview I cite a competitor instead of myself,” “On Claude I’m never mentioned for sector queries.” Three actions, absolute focus.
Pro tip

Repeat the same matrix every month with the same queries. Models, sources and behaviors change fast: without recurring measurement you can’t see whether you’re improving or losing positions.

10. Plan a monthly check of the same matrix. AI visibility changes quickly because models, sources and behaviors change. Without recurring measurement you’re working in the dark.

This matrix is a serious first step, but it remains a first step. The systematic mapping of visibility across all platforms — especially if you want to measure frequency, sentiment, competitive positioning — requires professional tools, broad-sample queries, continuous monitoring. What you find here is the conceptual map and a surface-level audit. The complete path, if you want to do it properly, is built calmly and with adequate instrumentation.

The thread that holds it all together

Visibility in AI answers is a six-floor building. On the ground floor are the engines — how they think and how they reason. On the first is trust — how the models decide who to trust. On the second the content structure — how to format pages so the AI extracts them. On the third the entity — how to exist as a recognizable node in the knowledge graphs. On the fourth the mentions — how you get the sources the AI cites to talk about you. On the fifth, where you are now, platform differentiation — because each one plays by its own rules and whoever blurs them loses on all of them.

The thread is always the same: visibility in AI answers isn’t built with a single strategy applied across the board. It’s built knowing that ChatGPT, Gemini, Perplexity, Claude and the long tail are different products, with different user bases, with different citation criteria. A good baseline strategy — floors one through four — opens the door to all of them. But to enter each one well you need fine calibration. The sixth floor, which I’ve just mapped for you, is exactly this: platform calibration. When it works, you can see it: you open four different engines, run the same query, and your name appears in all four — maybe with different nuances, but always present. This is the destination. If today you’re visible on one and invisible on three, you have a system to build. One platform at a time, with method.