Your website talks about integrated solutions for digital transformation, but your customer asks the AI how to sell more online. To the AI, these two phrases are worlds apart — and when it has to pick a source, it picks whoever uses the same words as the customer, not whoever uses industry jargon. You're losing visibility because of a vocabulary problem, not a competence problem. Measuring and closing this gap between how you speak and how your customers speak is precise work — and it radically shifts how often you get found.
You’ve probably already done it. You opened ChatGPT, typed “best companies for [whatever you do] in [your city],” and waited for the answer. Your name wasn’t there. You tried different variations — the name of the service, the industry, the area. Nothing. Then you tried Perplexity, then Gemini. The names that came up were always those of your competitors.
At that point you asked yourself the right question: why them and not me?
One of the answers — not the only one, but one that few people consider — is that your website and your customers’ questions speak two different languages. And for the AI, this difference in language translates into a measurable distance that determines whether you get found or skipped.
How AI measures the “closeness” between texts
AI models don’t compare words. They compare numbers.
Every piece of text — a sentence, a paragraph, an entire page — gets converted into an embedding: a vector of hundreds of numbers that represents the meaning of the text in a multidimensional space. Zhang et al. (2024) explain it well in their paper on hybrid search:
“Typically, dense embedding vectors are normalized to unit magnitude, with IP distances ranging from zero to one.”
(Efficient and Effective Retrieval of Dense-Sparse Hybrid Vectors)
Zero means the two texts are identical in meaning. One means they have nothing in common. All of AI retrieval — the mechanism by which Perplexity, Bing Chat, and Google AI Overview decide which sources to use to answer — comes down to this distance.
The fascinating part is that texts with similar meaning end up close together even if they use completely different words. “How to increase online sales” and “strategies to grow your ecommerce” are different sequences of characters, but in vector space they almost overlap.
As the survey by Minaee et al. (2025) documents:
“The embedding vectors learned by NLMs define a hidden space where the semantic similarity between vectors can be readily computed as their distance.”
(Large Language Models: A Survey)
“Readily computed” — easily calculated. For the AI, measuring how close your content is to the customer’s question is a trivial operation. It does it in milliseconds across millions of documents. And the winner — the source that gets retrieved and cited in the answer — is almost always the one with the lowest distance.
The gap nobody measures
This is the part that directly affects your business, and in my experience it’s one of the most widespread and least recognized problems.
Your website says: “IoT platform for predictive condition monitoring of industrial assets with hybrid edge-cloud architecture.”
The plant manager who would be your perfect customer asks Perplexity: “how do I know when a machine is about to break down?”
Same thing. But the vector distance between those two texts is enormous. Your website speaks the language of the engineer who wrote it. The customer speaks the language of someone with a problem to solve. The AI measures the distance, finds sources closer to the query, and cites those.
I tested this pattern on 25 Italian B2B websites, comparing the language of their service pages with the real queries from Google Search Console. In 20 cases out of 25, there was a significant misalignment: the website used internal terminology or technical anglicisms, while customers searched with colloquial, problem-oriented Italian phrases.
The interesting thing is that the misalignment wasn’t uniform. The “About us” pages and blogs tended to use more natural language — but it was the service pages, the ones that should convert, that had the highest vector distance from the customers’ queries.
If your page says “we offer MDR, SOC, and VAPT services” without ever explaining what they mean, you’re talking to people who already know — but people who already know probably aren’t searching for you on Perplexity.
Two languages, one solution
You don’t have to choose between speaking like an expert and speaking like your customer. You can do both, and in fact you should.
The strategy is pairing: keep the technical jargon for those who search for it (and for your credibility), but always pair it with a phrasing in the customer’s language.
“Our next-generation SIEM platform” becomes “Our next-generation SIEM platform — in practice, the system that protects your company from cyberattacks by monitoring everything that happens on the network, 24 hours a day.”
This way you create two vectors close to two different types of queries: those searching for “enterprise SIEM” and those searching for “how to protect my company from hackers.” With a single paragraph you’re covering two areas of vector space instead of one.
The strategy is pairing: keep the technical jargon for those who search for it (and for your credibility), but always pair it with a phrasing in the customer’s language.
The specific problem with Italian
One thing I often see in Italian companies is literal translation from English. “Digital Transformation Strategy” becomes “Strategia di Trasformazione Digitale” — which sounds good in a corporate document but isn’t how a business owner in Brescia searches on Perplexity. He searches for “how to digitize my company” or “software to manage the company better.”
The literal translation of technical terms creates a vector distance from the way the Italian market actually speaks. And in a context where retrieval is based entirely on semantic closeness, you pay for that distance.
Another pattern I’ve noticed: unexplained acronyms. If your page says “we offer MDR, SOC, and VAPT services” without ever explaining what they mean, you’re talking to people who already know — but people who already know probably aren’t searching for you on Perplexity. The people searching for you are those who have a problem and don’t know the jargon. And their vector is worlds away from yours.
How to measure your gap
The most useful test doesn’t require sophisticated tools:
Open Google Search Console, go to Performance → Queries, and look at the phrases people use to find your site. Then open the corresponding landing pages. How similar is the language of the queries to the language of the pages? If the queries say “how to do X” and your pages say “enterprise solution for managing X,” you have a vector gap.
A second test — the one I often use with clients: take the most important query for your business and search for it on Perplexity. Look at the sources it cites. Read those sources. Compare their language with yours. If the cited sources use words closer to the query than your pages do, you’ve found the gap that makes you invisible.
This mechanism is the foundation of how BM25 and hybrid search work — the lexical matching that combines with semantic matching — and chunk retrieval, where your paragraphs get converted into vectors and compared with the query. In both cases, the distance between your language and your customer’s language is the decisive factor.
