DEV Community: Nitish Kumar Yadav

The content changes that actually get you cited by AI (and the two that don't)

Nitish Kumar Yadav — Fri, 18 Sep 2026 12:49:43 +0000

Everybody's asking "how do I rank in AI search?" It's the wrong question. AI doesn't rank your page and send a click — it lifts a passage out of some source and stitches it into an answer. So the real question is narrower and more useful: what makes a model pull a sentence from your page instead of someone else's?

I build FixAEO, a tool for tracking whether AI engines cite and recommend brands, so I spend a lot of time looking at what actually gets pulled into answers versus what gets ignored. The good news: there's real research here, and it mostly contradicts the tidy "AEO checklist" everyone's copying off each other. Here are the content changes with evidence behind them — and, because I'd rather save you time than pad this out, the two popular ones the data says are a waste.

Quick honesty note: I'll cite sources as I go, and where something is correlation rather than proven cause, I'll say so. That's kind of the whole point of this post.

First, what "getting cited" actually is

When an engine answers "what's the best X for Y," it's not ranking a list of pages. It's composing prose and reaching into a handful of sources for facts, phrases, and names. To be in the answer, you need a chunk of your content that is easy to find, easy to trust, and easy to lift cleanly. Every change below serves one of those three.

That reframe matters because it kills a lot of busywork. You're not optimizing a page to rank. You're engineering quotable passages.

The changes that actually work

1. Put citations, quotations, and statistics into your content

This is the single most evidence-backed change, so it goes first. A GEO study out of Princeton and IIT Delhi (KDD 2024) tested this directly and found that adding citations, direct quotations, and statistics lifted a page's visibility in AI answers by up to 40%. Not vague "authority signals" — literally these three moves.

Concretely: when you make a claim, back it with a real number and name the primary source in the sentence. Quote an expert or a study directly. Swap "many companies struggle with X" for "68% of them did X in 2026, per [source]." A model composing an answer loves a clean, sourced, quotable stat because it makes its answer look credible. You're doing its homework for it.

2. Lead with a direct, self-contained answer

Models lift passages that stand on their own. If the answer to a question is smeared across three paragraphs and depends on the two above it, it's hard to extract cleanly — so it doesn't get extracted.

So: phrase your headings as the questions people actually ask, and put a crisp, complete answer in the first sentence or two underneath. Then elaborate. It's the inverted pyramid your journalism teacher nagged you about, and it happens to be exactly what an extraction step wants. Bonus: it's better for human skimmers too.

3. Be specific and first-hand

Generic content is interchangeable, and interchangeable content doesn't get chosen — there's no reason to pick yours. Original data, a concrete example, a real number from your own experience, an actual opinion: these are things only your page has, which makes your page the only place to get them.

This is also why "we asked an AI to write 50 blog posts" rarely moves AI visibility. If it's the average of everything already online, it adds nothing an engine couldn't already say. Say something the internet doesn't already know.

4. Get mentioned off your own site

Here's the uncomfortable one for people who only optimize their own pages. When Ahrefs ran correlations across 75,000 brands, web mentions (0.66) and especially YouTube mentions (0.73) tracked AI visibility far more closely than backlinks (0.22). That's correlation, not proof of cause — but the direction is clear, and it lines up with where the engines actually source. Profound's analysis of ~30 million citations found Google's AI Overviews lean heavily on Reddit and YouTube, not polished vendor pages.

So a real chunk of "content work" happens off your domain: being genuinely useful in the community threads where your buyers hang out, getting your product discussed on YouTube, earning mentions in third-party roundups. You can't fake this with a schema tag. You have to be worth talking about, in the places the models are reading.

5. Structure it so a machine can lift a clean chunk

Format is doing quiet work. Short paragraphs. One idea per section. Lists and tables where the content is actually a list or a comparison. Clear, descriptive subheads. All of it makes a self-contained, liftable unit that a model can drop into an answer without dragging in noise. Walls of text hide your best sentence inside a paragraph no engine will bother to untangle.

6. Keep it current

Freshness is a real signal, especially for the more search-like engines like Perplexity and for Google's AI Overviews. A page dated three years ago with 2023 stats is a weaker citation than the same page updated with 2026 numbers and a recent "last updated." Refresh your statistics, re-date genuinely revised pages, and prune claims that have gone stale. It's low-effort and it compounds.

The two changes that don't (sorry)

Now the myth-busting, because an honest guide has to tell you what to stop doing.

Schema markup is not a citation lever. This one's everywhere in AEO advice, and the data doesn't support it. Ahrefs added JSON-LD schema to 1,885 pages and measured roughly zero citation lift — AI Overview citations actually dipped 4.6%. Schema helps machines parse your page, which is table-stakes hygiene, but it is not a weight that earns you a recommendation. Add it because it's harmless and good practice; don't add it expecting to get cited.

llms.txt won't get you cited either. It's the shiny new checkbox, and it's unproven. Google has said outright it doesn't use it, and roughly 97% of llms.txt files receive zero AI requests. Its real use today is dev and IDE agents, not answer engines. Publish one if you want — zero downside — but crossing it off your list does not move your AI visibility.

While I'm here: a backlink-only push is also weaker than most people assume (that 0.22 correlation versus 0.66–0.73 for mentions). Links still matter for classic SEO; they're just not the AEO lever they're sold as.

How to know if any of it worked

Here's the trap with everything above: AI answers aren't repeatable, so you can't eyeball whether your changes worked. SparkToro and Gumshoe ran the same "best brand" prompt 2,961 times and got the identical list twice less than 1 in 100 times. One screenshot of ChatGPT quoting you after your rewrite proves nothing. You need a real before-and-after hit rate, sampled across many prompts and across engines.

That's the gap I built FixAEO to close. It runs your buyer questions across six engines — ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews, and Google AI Mode — and tracks your citation rate over time, so you can actually see whether "I added stats and rewrote the intro" moved the needle, and on which engines. It also shows the sources behind each answer, so you can see what the model is pulling instead of you and aim your next change at it.

Honest limits: on the entry plan those scans refresh every 72 hours (not daily), it's one seat and 15 tracked prompts, and it's early software. On price, the free scan is $0 and the full six-engine tracking is $29/mo, where a lot of tools in this space start around $95–$300. That's the pitch; the six changes above work whether or not you ever use it.

The takeaway

Getting cited by AI isn't a dark art, and it definitely isn't a schema tag. It's a short list of real content changes: cite your claims with quotes and stats, answer the question directly, be specific and first-hand, get mentioned where the engines read, structure for clean extraction, and keep it current. Skip the schema-and-llms.txt theater. Then measure, because you can't trust a single screenshot.

If you want a starting point, run a free scan on your own domain, see which pages (if any) AI already pulls from, and make the six changes to the ones it doesn't.

Run a free scan →

And if you make these changes and your citations don't move, tell me — I'd genuinely like to know where this breaks.

Beyond ChatGPT: how to get recommended by Perplexity, Gemini, and Google's AI Overviews too

Nitish Kumar Yadav — Fri, 18 Sep 2026 12:29:30 +0000

Almost every "how to show up in AI" post is really a "how to show up in ChatGPT" post. I get why — ChatGPT is the one everyone pictures. But if that's where your effort stops, you're optimizing for maybe two-thirds of the problem and ignoring the rest.

I build FixAEO, a tool for tracking and improving whether AI engines recommend your brand, so I stare at this data a lot. The single most useful thing I've learned: the engines don't pull from the same places. What gets you named in ChatGPT is not the same as what gets you into a Google AI Overview or a Perplexity answer. So "get recommended by AI" isn't one game. It's five or six games that happen to look alike.

Let me show you what I mean, then give you something you can actually act on.

The traffic is already splitting

Start with where the attention goes. In B2B during early 2026, DigitalApplied found AI referral traffic split roughly ChatGPT 63%, Claude 18.5%, Gemini 10.6%, Perplexity 7.3%. ChatGPT leads, clearly — but well over a third of your AI-referred visitors are coming from somewhere else. If you only ever check ChatGPT, that third is a blind spot you can't even see, let alone fix.

And that's just the chat assistants. The quiet giant is Google's AI Overviews, which were serving around 2.5 billion users a month by mid-2026 and now appear on north of 20% of US searches. For a lot of brands, the AI answer that matters most isn't in a chatbot at all — it's sitting on top of the Google results page they were already trying to rank on.

The part nobody tells you: each engine sources differently

Here's the finding that reframed how I think about this. Profound analyzed roughly 30 million citations and found that Wikipedia made up about 47.9% of ChatGPT's top-10 sources — but only 0.6% of all AI Overview citations. AI Overviews lean heavily on Reddit and YouTube instead. Same question, completely different source diet.

Ahrefs adds another angle: across AI-cited URLs, only about 12% also rank in Google's top 10 — but that average hides big per-engine differences. Perplexity overlapped with Google's top results around 28.6% of the time, while ChatGPT, Gemini, and Copilot were closer to 8%. Translation: Perplexity behaves the most like classic search, so your SEO work carries over there. The others are their own thing.

Put those together and the strategy writes itself: you can't optimize "for AI." You optimize per engine, because each one is drinking from a different well.

Engine by engine (what pulls, and what to do)

A quick tour. I'm describing how each engine tends to source — not ranking them; they all matter depending on where your buyers are.

ChatGPT leans on established, reference-grade sources (Wikipedia looms large in its top sources). The reasonable read: being a well-referenced, authoritative entity — consistent facts about you across the web, a clean Wikipedia/knowledge presence if you qualify — helps here. It's also the engine sending the most referral traffic, so it earns real attention.

Google AI Overviews is the volume king and sources very differently — more Reddit, more YouTube, more of the messy open web. Getting named here often means being talked about in community threads and video, not just on your own site. It's less "polish your homepage," more "be part of the conversation."

Perplexity is the most search-like of the bunch (highest overlap with Google's top 10) and leans on fresh, citable pages. If your classic SEO is decent and your pages are genuinely quotable, Perplexity is where that shows up fastest.

Gemini and Google AI Mode live in Google's ecosystem. AI Mode is fascinating — huge user numbers (around a billion by mid-2026) but still a tiny slice of actual US search queries so far. Worth tracking, not worth betting the quarter on yet.

Copilot rides Bing's index, so being properly indexed and reputable on Bing (not just Google) quietly matters.

You don't have to master all of these on day one. But you do need to know which ones you're winning and losing — and that's exactly the thing a ChatGPT-only view can't tell you.

The moves that work across all of them

Per-engine nuance aside, a few things move the needle everywhere. These are the ones with real evidence behind them.

Sample many prompts, per engine, over time. AI answers aren't repeatable — SparkToro and Gumshoe ran the same "best brand" prompt 2,961 times and got the identical list twice less than 1 in 100 times. One screenshot of any engine naming you is luck, not a measurement. You need a real hit rate per engine.
Get mentioned across the web, not just linked. Ahrefs' correlations across 75,000 brands found web mentions (0.66) and YouTube mentions (0.73) tracked AI visibility far more than backlinks (0.22). It's correlation, not proof — but "get talked about" beats "build links" as a bet.
Make your content quotable. A Princeton/IIT Delhi study (KDD 2024) found that adding citations, direct quotations, and statistics lifted content's AI visibility by up to 40%. Specific and sourced beats vague and salesy — across every engine.
Make sure the citation crawlers can actually reach you. The bots that drive citations aren't the training crawlers. Let in OAI-SearchBot and ChatGPT-User, PerplexityBot, Google-Extended, and the Claude search agents. If your robots.txt blocks those, you can't be recommended, period.

And two things to stop wasting effort on: schema markup doesn't get you cited (Ahrefs added JSON-LD to 1,885 pages and saw roughly zero citation lift), and llms.txt is unproven for citations (Google says it doesn't use it). Add them if you like — they're harmless — but don't expect them to earn you a recommendation.

Why you need to measure all of it (and where I come in)

Here's the honest problem with everything above: you cannot run six engines by hand. Checking ChatGPT, Gemini, Perplexity, Copilot, AI Overviews, and AI Mode — repeatedly, across a set of real buyer questions, and actually keeping track of the sources behind each — is a full-time job with a spreadsheet that falls apart in a week.

That's the gap I built FixAEO to fill. It runs your prompts across six engines — ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews, and Google AI Mode — and gives you a per-engine visibility score, so you can see at a glance that you're winning ChatGPT but invisible in AI Overviews, or vice versa. More importantly, it shows the citations and sources behind each answer and a competitor leaderboard, so you're not guessing why — you can see that AI Overviews is pulling from three Reddit threads you're not in, while Perplexity is quoting a competitor's comparison page. That's a per-engine to-do list, not a vague "improve your AI presence."

To be straight about the limits: on the entry plan those six engines refresh every 72 hours, not daily (daily is a higher tier), it's one seat and 15 tracked prompts, and it's early software — I'm still smoothing edges. Fair warning.

On price: most tools doing serious multi-engine tracking start somewhere around $95–$300 a month. FixAEO's free scan is $0 (it checks one engine, Gemini, so you can sanity-check fast), and the full six-engine tracking is $29/mo. I won't do a competitor teardown — their pricing pages are public — but the entry cost is the honest reason to look.

The takeaway

"Get recommended by ChatGPT" is a good goal that's too small. Your customers are asking Perplexity and Gemini, and reading AI Overviews on the results page, and each of those engines is choosing brands from a different set of sources. Optimize for one and you're flying blind on the rest.

So the move is simple to say and harder to do: measure across every engine, learn what each one pulls from, fix per-engine, and re-check. Start by finding out where you actually stand.

Run a free scan on your own domain and see whether AI names you today — then go find the engines where it doesn't.

Run a free scan →

And if you try it and something's off, tell me. At this stage that feedback is worth more than the $29.

How to Get Your Brand Recommended by AI in the United States (2026) — the FixAEO Playbook

Nitish Kumar Yadav — Fri, 18 Sep 2026 12:07:15 +0000

A few years ago, a customer in the United States looking for "the best project management tool for small teams" opened Google, skimmed a page of links, clicked two or three, and made up their own mind. Today a growing share of them type that same sentence into ChatGPT, or Gemini, or Perplexity, or they just read Google's AI Overview at the top of the results — and they take the answer. One paragraph. Three names. Done.

If your brand is one of those three names, you win a customer you never paid an ad to reach. If it isn't, you don't drop to page two. You don't exist in that conversation at all. There's no "keep scrolling." There's an answer, and a short list of brands inside it, and you're either on the list or you're invisible.

That shift has a name now — answer engine optimization (AEO), sometimes generative engine optimization (GEO) — and in the US market it's moved fast enough that treating it as "SEO's little cousin" is a mistake. I've spent the last stretch building a tool for exactly this problem (FixAEO), so I'll be upfront: this is a playbook and a pitch. But I've tried to make the playbook genuinely useful on its own — the kind of thing that works even if you never touch my product — and I'll tell you where the product falls short too. Fair deal.

The US numbers that made me build this

I don't want to drown you in statistics, but a handful of verified ones explain why this is urgent, specifically in the United States.

Start with the click that isn't happening. SparkToro and Similarweb found that in early 2026, about 68% of US Google searches ended without a single click to the open web — up from roughly 60% in 2024. Pew's data tells the same story from the user's side: when an AI summary shows up, only 8% of people click a result (versus 15% without one), and barely 1% click a link inside the summary. The traffic didn't move to a different page. It evaporated into the answer.

Meanwhile the answer itself is where attention now sits. ChatGPT crossed 800 million weekly active users in late 2025. Google's AI Overviews were serving something like 2.5 billion users a month by mid-2026. These aren't fringe surfaces; for a big slice of the US buying public, the AI answer is the search result.

Here's the part that should genuinely worry most brands. An analysis by Victorious looked at 107,011 AI responses across eight engines and found that 89.8% of brands — 159 out of 177 — had zero AI visibility. Not low. Zero. They were never named. And even when brands do get surfaced, Semrush and Kevin Indig found that 61.7% of AI citations don't actually name the brand — you get a link with no mention, or a mention with no link. Only 13.2% earn both.

Now weigh that against the value. Forrester's 2026 business-buying research found 94% of B2B buyers now use AI somewhere in their purchase process. And the traffic that does come through converts: Similarweb pegged ChatGPT referral traffic at around a 7.1% conversion rate — second only to paid search, and ahead of organic, direct, and social. So the people arriving from AI answers aren't tire-kickers. They arrived already half-sold, because an assistant they trust just recommended you.

Put simply: in the US, more of the demand is being decided inside AI answers, most brands are absent from those answers, and the ones present are converting unusually well. That gap is the opportunity.

What "recommended by AI" actually means

Let's define the goal precisely, because vague goals produce vague work.

"Getting recommended by AI" is not ranking. Ranking is about being findable on a page of options. Being recommended by AI is about being the source the model names, cites, or pulls from when it composes its answer to a real buyer question. The model has already done the choosing. Your job is to be one of the things it chose.

And it's genuinely a different game from SEO — I mean that technically, not as a slogan. Ahrefs looked at where AI-cited URLs actually rank in classic Google results and found that only about 12% of them sit in Google's top 10. Roughly 80% rank nowhere in the top 100. So the page that Google loves and the page that ChatGPT quotes are often not the same page. If you assume your SEO winners are automatically your AEO winners, you'll optimize the wrong things.

There's a US-specific wrinkle too: the answer changes by country. Ask the same question framed for a US user versus a UK or Indian one, and the models will lean on different sources and name different brands. So "are we recommended by AI?" isn't one global number — it's a per-market question, and for most of the brands reading this, the market that pays the bills is the US.

Why the usual playbook doesn't get you there

Before the how-to, let me clear out three things people waste money on, because an honest playbook has to include what doesn't work. All three are verified, and getting them wrong is expensive.

Schema markup is not a citation lever. This one stings because the whole industry sells it. Ahrefs added JSON-LD schema to 1,885 pages and measured roughly zero citation lift — AI Overview citations actually dipped 4.6%. Schema is table-stakes for machines parsing your page; it is not a weight that gets you recommended. Add it, sure, but don't expect it to move the needle.

llms.txt won't get you cited either. Google has said outright it doesn't use it, and around 97% of llms.txt files receive zero AI requests. It's a fine, zero-downside thing to publish, but claiming it drives citations is unsupported. (Its real use today is dev and IDE agents, not answer engines.)

Brand mentions beat backlinks. When Ahrefs ran correlations across 75,000 brands, web mentions (0.66) and especially YouTube mentions (0.73) tracked AI visibility far more closely than backlinks (0.22). Correlation isn't causation — nobody has proven the mechanism — but if you're deciding where to spend, "get talked about across the web" is a better bet than "build more links."

And one thing that is supported: a GEO study out of Princeton and IIT Delhi (KDD 2024) found that adding citations, direct quotations, and statistics to content lifted its visibility in AI answers by up to 40%. That's the shape of content that gets pulled into answers — specific, sourced, quotable. Keep that in your pocket; it matters later.

The playbook: five things it actually takes

Strip away the noise and getting recommended by AI in the US comes down to five moves. This is the framework I'd hold any tool — including mine — accountable to.

1. Measure across every engine, at scale, in your market. One question asked once tells you nothing, because AI answers aren't repeatable. SparkToro and Gumshoe ran the same "best brand" prompt 2,961 times and got the identical list twice less than 1 in 100 times, and the identical order less than 1 in 1,000. So a single screenshot of ChatGPT naming you is a coin flip, not a measurement. You need many real buyer prompts, run repeatedly, across the engines your customers actually use — ChatGPT, Gemini, Perplexity, Copilot, Google's AI Overviews and AI Mode — framed for the US. Only then do you get a true hit rate instead of a lucky moment.

And don't fall for the "just optimize for ChatGPT" advice. AI referral traffic is diversifying fast — in B2B during early 2026 it split roughly ChatGPT 63%, Claude 18.5%, Gemini 10.6%, Perplexity 7.3%. A ChatGPT-only view is blind to a third of your actual AI exposure.

2. Understand why you're absent. Being unnamed is a symptom. The cause lives in the sources — which pages and domains the model pulled from to build its answer, and which competitors it named instead of you. That's the lever. If Reddit threads and a competitor's comparison page are feeding the answer, no amount of tweaking your homepage will help; you need to influence those surfaces.

3. Turn findings into content the models will actually quote. Remember the Princeton finding — sourced, specific, quotable content wins. So the fix is rarely "write more blog posts." It's usually "rewrite this specific page so it directly answers this specific question, with evidence." And it has to be grounded in what the AI really cited, not in a content-mill guess.

4. Close the loop, on a schedule. This is the part everyone underestimates: it's not a project, it's a rhythm. Check, fix, re-check, forever. If a human has to do that by hand every week, it quietly dies. The brands that win automate the loop.

5. Keep the automation honest and safe. The moment you let AI write and publish on your behalf, you need a human review step you can't accidentally skip — not a black box quietly posting things in your name. Speed without a brake is how brands embarrass themselves.

That's the whole game. Now here's how the tool I built runs each step, specifically for the US market.

How FixAEO runs the playbook

I built FixAEO because I wanted to do all five of those steps without stitching together five tools and a spreadsheet. Here's how it maps, honestly.

FixAEO's AI Marketer, mid-run on a recipe — working from real citation data, with a confidence badge and a "review before use" footer.

Multi-engine tracking, framed for the US. You give FixAEO a set of real buyer questions — the prompts your customers would actually ask — and it runs them across six AI engines: ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews, and Google AI Mode. (Claude, Grok, and DeepSeek are there on the top tier, for nine total.) Crucially, it runs them as a US user: the US market gets the right geo-targeting on Google's surfaces and a US framing on the chat engines, so you're seeing the answers your American customers see — not a generic global blur. Out of that you get a Visibility Score: the share of AI answers in your space that actually name you. One number you can manage, tracked over time, per engine.

The why, not just the what. This is where FixAEO earns its keep. For every answer, it shows you the citations and sources behind it — the exact pages and domains the model leaned on. It tracks brand mentions with sentiment, so you know not just whether you're named but how you're talked about. It builds competitor leaderboards so you can see who's winning your topics and by how much. And it clusters everything by topic and shows the query fan-outs — the sub-questions the engines expand your query into. That's the difference between "we're invisible" and "we're invisible because Reddit and two competitors own the comparison queries, and here are the pages doing it."

Findings into fixes — grounded, not hallucinated. FixAEO ships with an AI Marketer: 17 ready-made agent templates, with five core content-and-report recipes I lean on most — get content recommendations, refresh an existing page, create a brief and first draft, optimize your lowest-cited page, and a weekly brand-health report. The part I actually care about is the grounding. It's evidence-gated: it won't let the model invent search volumes, competitor numbers, or outside facts. Every quote and citation it uses is machine-checked against sources it genuinely pulled, and anything that can't be backed gets dropped or the step fails. Given how much AEO advice is confidently made up, "the AI has to cite its work or shut up" was non-negotiable for me. And because the Princeton research says sourced, quotable content is what gets recommended, a tool that produces grounded drafts isn't just safer — it's producing exactly the shape of content the engines reward.

The AI Marketer's ready-made agent templates — the five core ones are the content-and-report recipes.

Automate the loop with a real agent builder. When the recipes aren't your exact shape, you can build your own agent as a node graph — wire up steps to pull your brand's data, run a skill (up to ten per agent), hand off to an integration, and gate on a human review. Then put it on a schedule: eight frequencies from hourly through weekdays, weekends, monthly, up to annually. So "check our US visibility, find the lowest-cited page, draft a fix, and drop it in front of me every Monday" becomes a thing that just happens. The integrations that ship today are deliberately few but real: Slack (post results to a channel), Google Docs (drop a draft into a doc), WordPress (push a draft post to your blog), and webhooks / a generic HTTP node for anything else.

Honest, safe automation — stated precisely. Where you place a review checkpoint, the run stops and cannot be skipped — not even in a test run. Anything that sends externally or is destructive requires a human every single time; you can't set it to "always allow." I'll be straight about the one gap: the builder doesn't force you to add a review step — if you wire an agent that sends something with no checkpoint, it warns you but it'll still run. Real guardrail, but opt-in. I'd rather tell you that than pretend it's foolproof.

A review checkpoint: the run stops here and can't be skipped — and external actions get one-time approval only.

Seeing the AI crawlers themselves. Beyond the answers, FixAEO shows Agent Analytics — which AI crawlers and agents are actually hitting your site. And it's worth knowing which ones matter: the bots that drive citations (not model training) are the search-side crawlers — OAI-SearchBot and ChatGPT-User, PerplexityBot, Google-Extended, and the Claude search agents. GPTBot and ClaudeBot are training-only. If those citation crawlers can't reach you, you can't be recommended, full stop — so seeing them is step zero. FixAEO also connects to Google Search Console and GA4, so you can tie AI visibility back to the traffic and conversions it actually drives.

And a free front door. You don't have to take any of this on faith. FixAEO runs a free, no-account scan (one engine, Google Gemini) plus heuristic hygiene checks — schema, robots.txt, llms.txt, meta. To be honest about those checks: they're table-stakes parsing hygiene, not citation magic (schema won't get you cited; I said so above). The free scan exists so you can see, in about a minute, whether AI knows your brand exists at all.

What a week with it actually looks like

Abstract features are boring, so here's the concrete loop for a US brand.

Monday morning, you open the dashboard and your US Visibility Score is 18% across the six engines — meaning fewer than one in five AI answers in your category name you. You drill into the competitor leaderboard and see a rival showing up in 47% of the same answers. You open their winning topic, look at the sources, and find the AI is leaning on a detailed comparison page they wrote and a couple of Reddit threads.

You fire the "optimize the lowest-cited page" recipe at your own thin comparison page. It comes back with a grounded rewrite — specific claims, real citations, the quotable structure the engines like — and because it's a review-gated recipe, it stops and waits for you to approve before it finalizes anything. You read it, tweak two lines, approve. You push the draft to WordPress with one step.

Then you set an agent to re-run those prompts every Monday and drop the new Visibility Score in your Slack. Three weeks later you check back and the score is 29% and climbing, and you can see which engines moved. That's the whole thing: measure in the US, find the why, ship a grounded fix, automate the recheck. Not magic — just the loop, run consistently.

Where it falls short (because I promised)

No honest playbook ends without the fine print, and no honest pitch hides it.

On the entry plan, scans refresh every 72 hours, not daily. If you need daily freshness, that's the higher tier.
The $29 plan is one seat, 15 tracked prompts, and up to two brands. It's built for a solo founder or a small team watching a couple of brands — not a 40-person agency running dozens.
It's early. I'm still shipping reliability fixes. If you throw something weird at it, you might find an edge I haven't.

If those are dealbreakers for you, genuinely no hard feelings — better you know now.

The price, plainly

Here's the honest wedge. Most tools doing serious AI-visibility work in this space start somewhere around $95 to $300 a month. FixAEO starts at $0 for the free scan, and the full multi-engine tracking plus the AI Marketer is $29 a month (or $25 billed annually). Same category of work, a fraction of the entry cost, and none of it hidden behind a "book a demo" wall. I won't run a competitor teardown — their pricing pages are public, go compare for yourself. That's kind of the point of doing this honestly.

The plans — everything above starts at $0 (free scan) or $29/mo.

Why $29 and not free for the real thing? Because the free scan checks a single engine so you can sanity-check whether AI knows you exist. The moment you want the full US picture — six engines, real citation data, and the agent that acts on it — $29 is the floor. For most solo founders and small teams, that's a rounding error against a single customer that an AI answer sends you.

The bottom line

The US discovery layer is quietly moving into AI answers. Most brands are absent from those answers, the traffic that does come through converts like paid search, and the work to get recommended is knowable: measure across engines in your market, find why you're absent, ship grounded content the models will quote, and automate the loop with a human in the driver's seat for anything risky.

You can do all of that by hand. I built FixAEO so you don't have to — and priced it so a solo founder can actually afford to.

So do one thing today: run a free scan on your own domain and see whether AI names you in the US right now. If it does, you've got a baseline to grow. If it doesn't, that's the gap quietly costing you customers — and now you know exactly what to do about it.

Run a free scan →

And if you try it and something's broken or dumb, tell me. At this stage that feedback is worth more to me than the $29.

The best tool to get your brand recommended by ChatGPT (2026)

Nitish Kumar Yadav — Fri, 18 Sep 2026 11:45:48 +0000

Someone asks ChatGPT "what's the best tool for X." It names three. If yours isn't one of them, you didn't lose a ranking — you were never in the conversation. There's no page two to climb. There's one answer, and a couple of names in it.

So "how do I get recommended by ChatGPT?" is a real question now, and people are searching for a tool to do it. I'll be straight with you up front: I built one (FixAEO). So treat this as a builder making his case, not a neutral review — I'll tell you where it's weak too.

But the more useful thing I can give you isn't a pitch. It's the checklist I'd use to judge any tool in this space — including mine. If you read nothing else, read that part.

First, the trap: "get recommended by ChatGPT" is not a ChatGPT problem

The phrasing fools people. Your buyers don't only ask ChatGPT. They ask Perplexity, Gemini, Google's AI Overviews and AI Mode, Copilot — often several, often without thinking about which. A tool that only watches ChatGPT gives you a confident number that's blind in five directions.

So rule zero: treat the engines as one surface, not a favourite. "Recommended by ChatGPT" really means "recommended by the AI your customer happens to ask." Judge tools on how many of those they actually cover.

The checklist: what a tool has to do

Here's what getting recommended by AI actually takes. Any tool worth paying for should do all five.

See it across engines, at scale. One question run once tells you nothing. You need many real prompts, run across the major engines, tracked over time — so you know your true hit rate, not a lucky screenshot.
Show you why. Being absent is a symptom. The tool should show which sources and pages the AI pulls from, and who gets named instead of you. That's the actual lever.
Turn findings into fixes. A dashboard that just reports is half a tool. The good ones help you produce the content changes that move the needle — grounded in what the AI actually cited, not made up.
Close the loop automatically. This is weekly work forever: check, fix, re-check. If the tool can run that loop on a schedule instead of you doing it by hand, that's the difference between a report and a result.
Be honest and safe about automation. If it writes or publishes, you want a human review step you can't accidentally skip — not a black box that posts on your behalf.

You can hold any product up to that list. Now here's how the one I built does against it.

The tool I built, measured against the checklist

1. Across engines, at scale. FixAEO runs your prompts across six engines — ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews, and Google AI Mode — not one. You track a set of real buyer questions and watch your citation rate move per engine, so "are we recommended?" becomes a number you can actually manage.

2. The why. It shows the citations and sources behind each answer, brand mentions with sentiment, and competitor leaderboards — who's getting named for your topics and which pages the models are pulling from. That's where you find the fix.

3. Findings into fixes. There's an AI Marketer with ready-made recipes — get content recommendations, refresh an existing page, build a brief and first draft, improve your least-cited page, weekly brand-health report — plus a bunch more templates. The part I actually care about: it's evidence-gated. It won't let the model invent search volumes or competitor numbers; every quote and citation is checked against sources it really pulled, and unbacked claims get dropped or the step fails. AI that has to cite its work or shut up.

4. Close the loop. You can build your own agent as a simple node graph and put it on a schedule, so the check-fix-recheck loop runs without you babysitting it.

5. Honest automation. Where you place a review checkpoint, the run stops and can't be skipped — not even in a test. Anything that sends externally or is destructive needs a human every time; you can't set "always allow" on it. And I'll be honest: the builder lets you skip adding a review step (it warns you, but it'll run). Real guardrail, but opt-in — I'd rather say that than pretend it's foolproof.

Where it loses (because every honest review has this part)

On the entry plan, scans refresh every 72 hours, not daily. Need daily freshness? That's a higher tier.
The $29 plan is one seat, 15 tracked prompts, up to 2 brands. It's built for a solo founder or small team, not a 40-person agency.
It's early. I'm still fixing rough edges. If you throw something weird at it, you might find one.

If those are dealbreakers, no hard feelings — genuinely.

The price part

Here's the honest wedge. Most tools that do serious AI-visibility work start somewhere around $95–$300 a month. FixAEO starts at $0 (a free single-engine scan, no card) and the full multi-engine tracking plus the AI Marketer is $29/mo (or $25 billed yearly). Same category of work, a fraction of the entry cost. I won't do a competitor teardown — their pricing pages are public, go compare. That's kind of the point.

Why $29 and not free for the real thing? Because the free scan checks one engine so you can sanity-check whether AI knows you exist. The moment you want the full picture — six engines, real citation data, the agent that acts on it — $29 is the floor, and it's not hidden behind a "talk to sales" wall.

So, what's the best tool?

For a solo founder or small team who wants to actually get recommended by ChatGPT and the rest without dropping a few hundred a month — I think it's the one I built, and I've told you exactly where it falls short so you can decide for yourself.

But don't take my word for it. Do one thing: run a free scan on your own domain and see whether AI even names you today. If it does, great — you've got a baseline. If it doesn't, that's the gap quietly costing you customers, and now you know.

Run a free scan →

And if you try it and something's dumb or broken, tell me. Right now that feedback is worth more to me than the $29.