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    Looking for a Tool to Optimize Your Brand's Performance in AI Models?

    There is no tool you pay for that magically optimizes your brand inside ChatGPT. Good tools tell you where you stand and which pages the model built its answer from — here is what the evidence says actually moves the needle, and what is being sold to you that does not.

    Etyma Team
    September 10, 2026
    8 min read
    Looking for a Tool to Optimize Your Brand's Performance in AI Models?

    If your CEO asks you "Why did ChatGPT recommend a competitor and not us?", your first reaction is figuring out how to give a satisfactory answer that buys you some time. The second one tends to be a frantic search for a tool that can fix it for you.

    The reality is that there is no tool or magic wand you pay for and your brand performance in AI is magically optimized. Not knowing or understanding this is where most of the money is wasted.

    Good tools tell you where you stand, and which pages the model built its answer from. The optimization itself is work, and most of that work happens somewhere other than your own website.

    What "optimizing for AI models" actually means

    Start with the mechanism, because it determines everything else. When someone asks an AI a commercial question — the kind where a brand could be recommended — it usually fetches live web results before answering. One study of ChatGPT grounding behaviour found 86.5% of commercial queries were grounded in live search, against 0.9% of informational ones.

    That is the single most useful thing to understand here. For the questions you care about, the model is not reciting something it memorised in training. It is reading a handful of pages in real time and summarising them. You are not trying to influence a neural network, you are trying to be in the set of pages it reads.

    Which also means the timelines are sane. Semrush published 81 new pages and tracked them: Google AI Mode cited 36% of them within 24 hours and 56% within a week, whereas ChatGPT climbed from 10% on day one to 42% by day 30. That was a high-authority domain, so your results will likely be slower — but you should start seeing an impact within a few weeks.

    What the evidence says actually moves the needle

    We went looking for what works, and the honest answer is that the tactics sold hardest are the ones with the weakest evidence behind them.

    Things with real evidence

    Ranking well in classic search still matters more than anything else. An academic study of 75 commercial queries found Google rank position was the dominant predictor of citation, with position-one pages cited in 43% of queries against 5% for position seven. Google itself says the quiet part out loud in its official guidance: "From Google Search's perspective, optimizing for generative AI search is optimizing for the search experience, and thus still SEO."

    Off-site presence correlates far more strongly than anything on your own domain. Across 75,000 brands, Ahrefs found YouTube mentions correlated with AI visibility at 0.737 and branded web mentions at 0.664, while backlinks managed roughly 0.19. Read that carefully, though. Ahrefs say themselves that correlation is not causation, and the sample skews to larger domains, so part of what those numbers measure is simply being a big brand.

    If you sell B2B software, the target is more specific again. An analysis of 1,263 evaluation-stage prompts across 250 software categories found 70.8% of citations pointed at URLs with the word "best" in the title tag. Your work queue is review sites and other people's listicles, not your homepage copy.

    Things being sold to you that do not work

    • llms.txt. Ahrefs checked server logs across 137,210 domains and found 97% of llms.txt files received zero requests, and no AI bot ever went looking for one that did not exist. Google's John Mueller put it plainly: "no AI system currently uses llms.txt".
    • Generic schema markup. A controlled test of 1,885 pages that added JSON-LD against 4,000 matched controls found no citation growth in ChatGPT or AI Mode, and a small decline in AI Overviews. The academic study above found the same null result, and Google states outright that structured data is not required.

    One correction while we are here, because we have quoted the number ourselves. That famous "40% visibility uplift" from the Princeton GEO paper is a relative increase in position-weighted word count inside a fixed set of five documents, not 40% more traffic. A critical survey of the field rates the evidence that citations predict clicks or conversions as very low confidence, so anyone selling a revenue model built on AI citations is ahead of the science.

    So what should the tool actually do?

    A tool earns its keep on four jobs. Judge the demo on these and ignore the rest of the screen.

    • Sample often, across every engine. Answers move run to run and month to month, so a weekly check on two models is measuring noise. Daily collection across the full set is the minimum.
    • Show you the sources behind each answer. This is the whole game. A visibility score tells you that you have a problem; the cited domains tell you which twelve pages to go and influence, and whether it is a Reddit thread, a competitor's comparison page or a listicle you are not on.
    • Measure honestly, without forcing search. This one is under-discussed. Research from Graphite found that forcing web search in tracking tools shifted measured brand visibility by an average of 20 percentage points on prompts that rarely trigger search naturally. Optimise against those and you are chasing citations real users never see. It is vendor research arguing against its own category's standard practice, which is usually a sign it is worth reading.
    • Report it to people who will never read this article. A CMO or a client needs a page they understand, monthly, with a trend line and a sentence on what changed. Unglamorous, and the reason half these subscriptions get renewed.

    Which tool, for which situation

    If your job is watching perception and finding the sources to work on, across one brand or fifteen, Etyma is the one we would start with. It queries ChatGPT, Gemini, Google AI Overview, Claude and Perplexity separately every 24 hours on every plan, stores the full answers, and scores each for cited sources by weight and relevance. Nothing goes on your site, and several clients run from one account with white-label reports. That is the first two jobs above, which is what most teams need before anything clever.

    If you want to know what people are actually asking rather than what you assume they ask, Profound has panel data from opted-in consumers that nobody else has, plus server-log analytics showing which AI crawlers reached which pages. Etyma does neither, so if your bottleneck is technical rather than editorial, buy that instead.

    If you are enterprise and want an execution layer rather than a dashboard, Scrunch generates a machine-readable version of your site for agents and monitors crawl health. Worth checking either way, because as of Vercel's crawler analysis no major AI crawler executed JavaScript, so a client-side rendered site may simply not be readable at all.

    If you are learning the space on a single brand and do not want a sales call, Otterly starts at $29 a month. Check the add-on list before you compare that price with anyone.

    What we would ask on the demo

    • Do you force web search when you run prompts, and can I see both versions?
    • How many engines do I get on this plan, at this price, with no add-ons?
    • Show me one answer, and every source behind it, ranked.
    • What does the monthly client report look like, before anyone edits it?

    Then give whatever you buy ninety days before judging it. The signal is noisy enough that a month of data tells you almost nothing.

    The reason to bother at all is the one behavioural number here that survives scrutiny: when an AI assistant mentions a vendor, 71% of B2B buyers go and visit that vendor's website. That is the mechanism connecting any of this to a pipeline. Everything else is measurement, and measurement is not the same thing as being recommended. Buy the instrument, then go and do the work it points at.

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