Insights on AI Search Engine Optimization, GEO & AEO

We studied 1000 entities, across 17 LLM’s and Chabots using 5070 prompts, and getting 86,190 answers to understand what LLMs know vs what they say and why attention inside AI looks wildly unequal. Most executives now hear some version of this line every week: “We need to be more visible

Most execs hear the same advice: “Get a Wikipedia page. Then AI will know you.” That advice is incomplete. In a benchmark of 1,000 entities tested across 17 chatbots/LLMs (with 5,070 total prompts and 86,190 total answers), we found a group of entities that were recognized by many models but

AI has become a discovery channel. People ask chatbots who the “top vendors” are, what brands are “trusted,” and which options are “best.” If your organization doesn’t show up, you don’t just miss attention you miss consideration. But here’s the twist from the LLMtel 1,000-entity study: a lot of organizations

A practical lesson in how AI “knows” your company even when Wikipedia is missing. Executive summary (for busy leaders) We ran a 1,000‑entity benchmark across 17 chatbots / large language models (LLMs) using LLMtel-style scoring. In the Top 25 scoring entities in one of the segments, 24 out of 25

Executive summary (for busy leaders) Using our proprietary data from LLMtel.com, we ran a 1,000-entity benchmark across 17 chatbots/LLMs where we reviewed 86,190 answers and found a hidden problem that hits brands, vendors, and even large organizations: 319 entities were “known” by at least one LLM but were never named

Most leadership teams now ask some version of this:“Are we showing up in ChatGPT and other LLMs?”The honest answer is usually: not much.Not because the models can’t recognize your company.But because most brands don’t get named in answers, even when a model could name them. There is a huge drop

What a 1,000‑entity / 17‑model benchmark teaches us about “being known” vs “being named” in LLM answers If you’ve ever asked, “Will AI recommend our company?” you’re already asking the right question. But there’s a catch: LLMs don’t work in a single step. In our benchmark, we saw a pattern

A New AI Awareness Funnel for Understanding How LLMs Treat Brands Most “LLM visibility” talk confuses being known with being named, and being named with being trusted.Our 1,000-entity / 17-model benchmark shows that AI systems treat brands in five distinct, predictable ways mapped cleanly by the AI Awareness Funnel: Invisible

Most executives are asking a simple question:“Do the major AI chatbots know our brand?”But in practice, that question hides two different questions: Recognition: If we ask the model about us, does it know who we are? Activation:When people ask relevant questions, does the model actually bring us up? If you

Think of this as the classic marketing funnel, re-built for LLMs.As AI systems become gatekeepers in discovery, recommendations, and decision-making, a new question has emerged for every leadership team:“Where does our brand stand inside the AI ecosystem?”The AI Awareness Funnel provides a simple, accurate way to answer that question.It breaks

If an AI system can’t link your name, it can’t find you. And if it can’t find you, it will act like you don’t exist.That is why real companies show up as “unknown” in LLM answers. Not because they are small. Not because the model is “bad.” But because the

In the LLMtel 1,000 entity study, one result should make every executive pause: A lot of entities are known by AI – but still never get mentioned. In the data, 319 entities fall into a bucket we’ve been calling “Ignored”: Known & Not Named = recognized by at least one

Executive summary (the part you can read in 60 seconds) When a chatbot “doesn’t know your company,” the problem is often not awareness. It’s name matching. Small changes in punctuation, abbreviations, or legal naming can make an entity look like a different thing. In the LLMtel 1,000-entity benchmark (17 LLMs

The definitive content guide Ever wondered how the world’s smartest AIs “see” your brand—and what to do once you know? LLMtel gives you not just a one-off snapshot, but an ongoing roadmap to owning your story in every new AI release. Think of it like running a health check-up—but for

Base Knowledge (Static Training Data) ChatGPT’s out-of-the-box knowledge comes entirely from its pretraining on large text datasets. This training includes publicly available internet content (e.g. web pages, books) and other data OpenAI had access to (source). The model’s knowledge is essentially static up to a cutoff date – for example,

Understanding Knowledge Graphs and Knowledge Panels for SEO and AI Introduction Knowledge graphs and knowledge panels have become fundamental to how modern search engines organize and present information. A knowledge graph is essentially a vast networked database of facts about entities (people, places, organizations, things) and the relationships between them.

This SOP shows you exactly how to strip “AI slop” from drafts and make ChatGPT write in a cleaner, more human voice by default. You’ll get two ready-to-use prompts (full + compact), a quick QA checklist, and steps to save the style in Customize ChatGPT so every new chat follows

The definitive content guide An LLM is like a person that has gone to school. It learns everything up to a certain point, say for example being a junior in high school.  It learns math, science, and history up to that point. And that’s all it knows. It doesn’t know

If your information was crawled by a model and became part of its knowledge today, it can take a year or more for that information to be shown in the next model release. This is the basic 3-step process: Step : 1 (approximately 6 months):  Information is gathered by web-crawlers

How AI “Learns” to TalkThink of ChatGPT like a smart robot that writes and answers questions. It doesn’t actually understand the world the way people do, but it reads so much text books, websites, articles that it can sound really smart and almost human. To make sense of how it