Strategy
How AI categorizes your brand (and why that category determines everything)
Before recommending, AI classifies. If it can't classify you clearly, it won't recommend you in any category.
There's a step before any AI recommendation that few brands consider: categorization. Before a model decides whether to include you in a response, it needs to understand which category to put you in. Are you a B2B or B2C tool? A premium or accessible service? A specialist or a generalist? If AI can't answer those questions clearly, the probability of it recommending you drops dramatically. And the problem is that many brands, without realizing it, are sending contradictory signals that make that categorization impossible.
What is AI categorization and how does it work
When an LLM processes information about a brand, it builds an internal representation of what that brand is, what it does, who it serves and in what contexts it's relevant. That representation is what we could call its categorization.
Categorization isn't a field in a database — it's a semantic construct that emerges from all the signals the model has processed. If your website says you're a 'comprehensive digital transformation solution', your LinkedIn talks about 'innovation and technology' and the media that mention you do so in 'business consulting' contexts, the model builds a broad and blurry categorization.
The problem with blurry categorization is that it doesn't trigger specific recommendations. AI recommends in response to specific queries: 'best tool for X', 'solution for Y', 'experts in Z'. If your categorization doesn't match any of those queries with sufficient specificity, you don't appear.
The most frequent categorization mistakes
Trying to be everything to everyone. The most common trap. A brand that tries to cover too many categories ends up being a reference in none. AI reflects exactly that: it mentions you in various contexts but never as the top option in any.
Right category, wrong market. A brand operating in Spain whose online communication is mostly in English may be categorized by AI in English-speaking markets — making it invisible for searches in Spanish about its sector.
Past category. Brands that have pivoted their business model but haven't updated their signals. AI continues to categorize them according to what they were, not what they are.
Wrong subcategory. An HR consultancy that wants to appear in 'digital transformation' searches needs to build specific signals for that subcategory — being a consultancy in general isn't enough.
How to define and reinforce your correct category
The process always starts with a question: in which specific query do you want to appear when someone asks AI? Not a generic category — a real query, as your ideal customer would make it.
With that query as the objective, the work consists of aligning all signals to point in that direction: the website's content architecture, the language in owned channels, mentions in external media and presence in sector reference platforms.
The key is specificity. 'Business management software' is too broad. 'Project management software for architecture firms' is a category where you can be a reference. AI rewards specialization with greater visibility in the queries that matter.
Categorization is the foundation of everything. Before thinking about SEO, content or AI PR, you need to be clear about the category where you want to be the reference — and ensure all your signals point toward it. Without that, the rest of the work doesn't hold up.
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