By Mehdi HassanHow does AEO for AI startups differ from regular AEO?
AEO for AI startups follows the same foundations as any answer engine optimization, but the competitive position is unusual: you're trying to get recommended by the assistants your product is often built on, in a category that may be months old. That changes what you publish first.
Three problems are specific to AI companies. The category is often new or unnamed, so an assistant has no stable phrase to file you under. The model's training data lags behind launches, so a startup that shipped last quarter may not exist in what the model already knows, which pushes the work onto live web search. And the general assistant is itself an alternative: a buyer asking about an AI writing tool may be told to just use the assistant they're talking to.
Our hub on getting recommended by ChatGPT covers the general method. This guide is the version for AI companies, in the order we'd do it.
Priority | Work | Why it comes in this order |
|---|---|---|
1 | Category definition on your money pages | Nothing else works if assistants can't say what you are |
2 | Comparison pages | Buyers ask assistants to compare, and you supply the facts |
3 | Third-party mentions | Assistants recommend what independent sources agree on |
4 | llms.txt and readable docs | Helps developer tools read you; no proven citation lift |
5 | Monthly prompt audit | Shows whether 1 to 4 changed the answers |
How do you define your category so AI assistants can repeat it?
Define your category with one sentence that names an existing category noun, the buyer and the job, then use it word for word everywhere. Assistants recommend brands that sources describe consistently, and a sentence like 'Acme is an AI meeting assistant for sales teams that writes CRM notes after every call' is easy to repeat. 'Acme is the intelligence layer for revenue' isn't.
- Borrow a category noun buyers already type. 'AI support agent', 'AI code review tool' or 'contract review software' gives the assistant a shelf to put you on. A coined category nobody searches leaves you out of the prompts that matter.
- Add a qualifier you can own. The buyer, industry or workflow narrows the category to one where you can be the obvious answer: 'for Shopify stores', 'for SOC 2 audits'.
- Put it on the homepage first screen, the about page, pricing page, LinkedIn, GitHub and every directory listing, with the same wording.
- State what the model does and doesn't do. Assistants and buyers both need limits, data handling and the input and output in plain words.
The positioning work behind that sentence is in our guide to AI startup website strategy. For AEO, the rule is narrower: pick one sentence and stop varying it. Whether you call this AEO or GEO makes no difference, as SEO vs AEO vs GEO explains.
Which comparison pages should an AI startup publish?
Publish comparison pages for the three alternatives an AI buyer actually weighs: the established vendor, building it yourself on a model API, and using a general assistant directly. Buyers ask assistants exactly these questions, and an honest page you wrote is often the clearest source of facts about your side of the comparison.
Comparison | The buyer's real question | What the page must say |
|---|---|---|
You vs the incumbent | Is the new tool better than what we know? | Where you're better, where they are, and who should pick each |
You vs building on a model API | Can our engineers just do this? | What you handle that a prototype doesn't: evals, edge cases, security, upkeep |
You vs a general assistant | Why not just use ChatGPT or Claude? | The workflow, integrations, data controls and consistency you add |
You vs another startup | Which of these new tools? | Specific differences in scope, pricing model and fit |
Keep them fair. Peec's analysis of 232,000 citations found self-promotional 'best X' lists make up only about 4% of ChatGPT citations, so a page that ranks itself first at everything is unlikely to be quoted. A page that says 'choose the incumbent if you need X' is more credible. TripleDart's 2026 study of 28 B2B SaaS companies also found pricing comparisons were one of two blog formats that converted AI-referred visitors, at 1.5%.
Hypothetical example: the 'why not just use ChatGPT' page
For example, suppose an AI startup sells a tool that drafts security questionnaire answers from a company's policies, and prospects keep asking why they can't paste the questionnaire into a general assistant.
The page opens with a two-sentence answer: a general assistant can draft answers, but it doesn't keep an approved answer library, cite the source policy or route answers to reviewers. A table compares the two on those points and data retention, and the verdict says a general assistant is fine for one questionnaire a quarter. Assistants asked the same question now have a fair source to quote.
Where should AI startups earn third-party mentions?
Earn mentions where AI buyers and developers already compare tools, because assistants recommend brands that independent sources agree on. Ahrefs' study of 75,000 brands found web mentions correlated 0.664 with AI Overview visibility, against 0.218 for backlinks, so being named in the right context matters more than collecting links.
- Integration and marketplace listings. Partner directories for the tools you connect to, and any marketplace run by the model or cloud providers you build on, with your category sentence in the description.
- Developer surfaces. A public GitHub repo, SDK or example project with a clear README, plus answers in developer communities where people ask for tool recommendations.
- Review platforms in your category, with customers describing the use case in their own words, not only giving a star rating.
- Newsletters, podcasts and roundups that cover AI tools for your buyer's job, where you explain the problem rather than the model.
- Original data. A benchmark, an eval or a small study others can cite, published on a page with the method and numbers in text.
ChatGPT cites about 15 sources per answer, according to the Semrush AI Visibility Index, so you need to be named in several of the sources it reads for your category prompt, not to dominate one. Perplexity leans on long, detailed pages, covered in our Perplexity SEO guide.
Does an AI startup need an llms.txt file?
An AI startup with developer docs should add an llms.txt file, but not for citations. The 2026 evidence shows no citation lift: SE Ranking's study of 300,000 domains found no measurable effect, and Ahrefs found 97% of llms.txt files received zero requests. The case for it is that developers point coding assistants at your docs, and a clean index helps those tools find the right pages.
That's why it's fourth, not first. If you sell an API, SDK or developer platform, write an llms.txt linking your quickstart, API reference, pricing and limits, and update it with releases. For a marketing site alone, it's optional. Our free llms.txt generator formats the file, and other tactics that don't move citations are in AEO myths.
How do you check whether assistants recommend your AI startup?
Check by running your buyers' category prompts in ChatGPT, Perplexity, Claude and Gemini every month and recording who gets named and which sources are cited. For AI startups, add prompts that pit you against the general assistant, since that's a comparison competitors won't cover for you.
Write 20–30 prompts
Category prompts ('best AI support agent for Shopify'), comparison prompts ('Acme vs its main competitor', 'Acme vs just using ChatGPT') and fact prompts ('how much does Acme cost').
Run them with web search on
Use a fresh session, run each prompt twice because answers vary, and test at least two assistants.
Record the answer and the sources
Note whether you're named, how you're described and which URLs are cited. The cited URLs are your mention targets.
Fix the cause, not the symptom
Wrong description: fix your category sentence. Missing from comparisons: publish the page. Absent from sources: earn mentions there.
Our guide on checking what ChatGPT says about your company has the audit template. To score your site's readiness first, run our free AEO score checker, and if you outgrow a spreadsheet, see AI visibility tools compared.
What should happen when an assistant sends you a visitor?
When an assistant sends a visitor, the page they land on should confirm what the assistant said within the first screen: the same category, the same buyer and the same headline claim. In TripleDart's study, homepages earned 81.7% of AI-attributed leads, so that's usually the page doing the work.
For AI products, add proof the assistant can't give: a real product demo, pricing or the pricing model, data handling answers and supported integrations. These visitors have often already compared you, so they want confirmation and a next step. Whether they convert is covered in does AI search traffic convert, and our generative AI industry page shows how we approach sites for AI companies.
