AI doesn’t decide your competitor is better. It just learned their story first, and entity optimization is how you rewrite which story wins.
Ask ChatGPT which project management tool is best for a 50-person startup. It probably names Asana or Monday before it ever considers your product, even if your product is objectively a better fit. That’s not a keyword problem. You don’t have a “best project management tool” keyword issue. You have a consensus problem, and no amount of prompt tweaking fixes that.
Entity Optimization ≠ Prompt Hacking
A lot of what gets sold as “entity optimization” right now is really just prompt engineering with a fancier name. Add schema markup. Get cited on a few “best of” lists. Answer FAQ-style questions on your site so an LLM has something clean to quote. None of that is wrong, exactly. It’s just aimed at the wrong layer.
Entity optimization, done right, isn’t about making your brand easier for AI to parse in a single answer. It’s about making your brand the correct answer according to the body of human-written material the model already learned from. AI models don’t evaluate your product fresh every time someone asks a question. They retrieve a compressed version of what the internet has already agreed on. If the internet’s consensus says Competitor X is the standard, the model repeats that, regardless of how well-structured your FAQ page is.
This is why brands get frustrated. They do the AEO checklist. They fix their meta tags, add structured data, publish comparison pages. Mentions might tick up slightly. But when someone asks “what’s the best CRM for a small agency,” their competitor still gets recommended first, with more conviction and more detail. The checklist didn’t touch the actual thing driving the answer.
The Consensus Layer: Why AI Picks Them Before It Ever Sees You
Every category has a story that predates any individual AI query. Enterprise search has decided Elastic is the technical default and Algolia is the easy one. B2B analytics has decided Mixpanel is for product teams and Amplitude is for growth teams. Nobody voted on this. It accumulated over years of reviews, Reddit threads, analyst reports, comparison blogs, and conference talks, until it hardened into something close to fact.
When a model gets trained or when it retrieves live sources for an answer, it’s not asking “which of these products is actually better for this specific user.” It’s asking “what does the existing record say is true about this category,” and then applying that frame to whoever’s being compared. That’s why your competitor gets recommended even when your product wins the feature comparison. The model isn’t grading features. It’s inheriting a frame, and your competitor already owns it.
This is the pillar underneath everything else here: AI is downstream of consensus. It doesn’t originate opinions about your category. It reflects them back, compressed and confident.
How AI Learns the Narrative About Your Category (And Why Keywords Miss It)
Keywords describe what people type into search boxes. Narrative describes what people (and now models) believe to be true. Those aren’t the same thing, and optimizing for one doesn’t move the other.
Try this: ask an AI assistant “why would someone choose [your brand] over [competitor]?” Read the answer closely. It will almost always default to whatever framing shows up most consistently across the sources it draws on: G2 categories, review site summaries, comparison articles, forum consensus, analyst positioning. If those sources have quietly decided your competitor is “the enterprise-grade option” and you’re “the budget pick,” the model will say exactly that, confidently, even if your pricing and your competitor’s are within 10% of each other.
Keywords can’t touch this because the narrative isn’t stored in search volume. It’s stored in the accumulated framing across hundreds of pieces of third-party content the model has absorbed. Fixing your on-page SEO doesn’t touch a single one of those sources.
Measuring Narrative Share vs. Mention Count
Most AI-visibility tools tell you whether you got mentioned. That’s a start, but it’s a vanity metric dressed up as insight. Getting mentioned in an AI answer and winning that answer are different outcomes. You can show up in the list of five tools and still get positioned as the afterthought, the “also consider” line at the end.
What actually matters is narrative share: whose framing the model adopted when it built the answer. Two brands can both get mentioned in response to “best email marketing tool for ecommerce,” and one gets described as the category standard while the other gets described as a cheaper alternative. Mention count treats those as identical outcomes. Narrative share doesn’t. It tells you whose story is actually running the room.
The Three Sources AI Draws From (And Which One Actually Moves Recommendations)
Broadly, AI answers about your category get shaped by three kinds of sources. First, structured data: review sites, comparison databases, G2 grids. Second, editorial content: blog posts, “best of” roundups, analyst write-ups. Third, community sentiment: Reddit, forums, Twitter/X threads, Discord servers, the messy unfiltered stuff.
Structured data is easy to game short-term and easy for models to discount long-term. Editorial content moves the needle more because it’s where the actual comparisons and framing live, the sentences that get paraphrased into AI answers. Community sentiment moves it slowest but hardest: once Reddit has decided your competitor is “the real deal” and you’re “fine but overpriced,” that opinion shows up in AI answers for years, because models treat community consensus as a strong signal of ground truth.
If you’re only optimizing structured data, you’re polishing the least influential lever.
How to Rewrite Consensus Before AI Catches Up
Rewriting the frame starts with knowing what frame currently exists, source by source. Not “are we mentioned,” but “what story is being told about us, where, and by whom, and how far has that story already spread into AI training data and retrieval.” Then it’s a matter of shipping content, positioning, and outreach that targets the specific sources holding the wrong frame in place, not a generic content calendar.
This is slow work compared to a schema update. It’s also the only work that changes what the model says six months from now instead of what it says in a single cached answer today.
If you want to see whose frame is actually running your category’s AI answers right now, that’s the read Mavel gives you: not another mention tracker, but the narrative underneath it and a prioritized list of what to ship to change it. Come see what story AI is telling about you before your competitor gets to tell it for another year.
