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Industry guide

AI SEO for AI Startups: How to Get Recommended by ChatGPT and Google AI Overviews

By Rahil Jain, founder of Chalk LabsUpdated How we research

The short answer

An AI startup gets recommended when AI engines can find current, specific facts about the product and see them confirmed elsewhere. Publish plain-text docs, pricing, limits and honest comparisons, and date every page. Facts in this field go stale fast, so refresh key pages often, and never claim capabilities the product cannot show.

  • ChatGPT often adds the year to its searches: '2026' appeared in 22.9% of fan-out queries Ahrefs analysed in September 2026. 1
  • The median page lost half its AI citation share within 11 days of peaking, so freshness is a running cost. 2
  • On comparison questions, official sites took 46.4% of ChatGPT citations against 63.4% on factual ones. 1
  • Publish docs, pricing, limits and comparisons as plain HTML text with dates, and keep every AI capability claim provable. 910
  • We have no published AI-citation results for AI startups, and we say so below.
Why it matters in AI Startups
  • About half of surveyed software buyers start research with an AI chatbot more often than Google, and 69% were led to a different vendor 3.
  • Facts go stale fast: the median page lost half its AI citation share within 11 days of peaking 2.
  • On comparison questions, official sites got 46.4% of ChatGPT citations, so outside coverage shapes your shortlist 1.
  • Developers distrust AI output (45.7%), so they check answers against your docs 5.

What buyers in AI Startups ask AI

Pick a question to see what the buyer wants and what to publish so an AI can cite you.

  • What they want
    Shortlist a framework for a support use case
    What to publish
    A dated overview of frameworks, a 'can and cannot do' page, and a docs guide for human handoff

    Searches we expect (modelled by Chalk Labs, not observed)

    • best AI agent frameworks 2026
    • AI agent framework customer support open source
    • agent framework human handoff support tickets
    • AI agent framework pricing hosting cost

What could demo and trial requests be worth?

Enter your own numbers to see how visits to your docs, pricing and comparison pages turn into demo or trial requests, paying accounts and first-year revenue each month.

People who arrive from Google, ChatGPT, Perplexity and similar.
Form fills, calls and emails, as a share of visits.

What this adds up to

About 1.8 customers a month and $10,800 in revenue. After a monthly cost of $2,500, that leaves $8,300.

About 1.8 customers a month and $10,800 in revenue. After a monthly cost of $2,500, that leaves $8,300.
Enquiries a month
22.5
Customers a month
1.8
Revenue a month
$10,800
Revenue minus cost
$8,300
Return multiple
4.3x
Revenue for every $1 spent.
Visits needed to break even
348
Visits a month to cover the cost.

How it is worked out

  • Enquiries = visits × enquiry rate.
  • Customers = enquiries × close rate.
  • Revenue = customers × average customer value.
  • Revenue minus cost = revenue − monthly cost.
  • Return multiple = revenue ÷ monthly cost.
  • Visits to break even = monthly cost ÷ revenue per visit. Revenue per visit = enquiry rate × close rate × customer value.
Size this for my company (free proposal)

How do buyers and builders use AI to choose an AI product?

Both groups ask AI first and verify second. In a March 2026 G2 survey of 1,076 B2B decision-makers, 51% said they start software research with an AI chatbot more often than with Google, and 69% said a chatbot led them to a different vendor than planned 3. A Gartner survey of 645 buyers found 45% used generative AI in a recent purchase, and 69% prefer to validate its output with a sales rep 4.

Developers behave the same way. In Stack Overflow's 2025 survey, 84% of respondents used or planned to use AI tools, and 47.1% used them daily 5. Trust is low: 45.7% somewhat or highly distrust the accuracy of AI output, and 66% name 'almost right, but not quite' answers as their top frustration 5.

Read these with care. G2 sells review and marketing services, the Gartner survey covers B2B buyers in general, and neither study says which sources the chatbots cited. For a startup, the useful lesson is plain: a buyer will check what the AI said against your own docs.

45.7%of developers somewhat or highly distrust the accuracy of AI output (Stack Overflow 2025 survey)5

What is hidden inside 'best AI agent framework for customer support'?

Five separate questions are hidden in that one prompt: what a framework is, which languages it supports, how it handles human handoff, how you test it, and what it costs to run. G2 found that category prompts ('best X for Y') make up 33% of first prompts for software, ahead of rival comparisons at 31% 3. AI engines split them into smaller searches, which is called query fan-out. What is query fan-out explains it in full.

We did not observe a fan-out for this prompt. The table below is our modelled reading of it. It shows where a startup's pages could match, and it is not what any engine actually ran.

In Stack Overflow's 2025 survey, among the developers who build AI agents (about 8% of respondents), Ollama (51.1%) and LangChain (32.9%) were the most-used tools 5. Ollama runs models locally and LangChain is an agent library, so this shows what a small group uses, not which framework is best for support.

Modelled sub-questions behind one buyer prompt (not observed)
Hidden questionLikely searchPage that answers it
Which frameworks exist?best AI agent frameworks 2026A dated overview that includes rivals fairly
Does it fit our stack?agent framework Python TypeScript supportA languages and integrations page, one line each
Can a human take over?AI agent human handoff support ticketsA docs guide with a working code sample
How do we test it?how to evaluate customer support AI agentAn evaluation guide with your own test method
What will it cost?AI agent framework pricing hosting costA pricing page with limits and a 'checked on' date

Why do facts about AI products go stale so fast, and what does that mean for pages?

They go stale because models, prices and features change monthly, and engines favour pages that look current. Ahrefs analysed 66.6 million free, logged-out ChatGPT responses from August and September 2026. In September, '2026' was the most common modifier in fan-out queries at 22.9%, followed by 'official' at 13.1% (of English fan-out queries) and 'price' at 8.9% 1.

Citations also fade quickly. Profound tracked 883,000 pages across seven answer engines and found the median page lost half its citation share within 11 days of its peak 2. Among pages that stayed cited, the median half-life was 27 to 29 days, and comparison, listicle and review pages lasted longest at 29 days 2.

The practical rule is to put a visible 'last checked' date on pricing, model, limits and comparison pages, and to give each one an owner. Pages cited by five or more engines held up better: 6.8% were still above half their peak at day 56, against 1.2% for pages cited by one engine 2. See how to refresh old content for AI search.

What do AI engines look at when an AI product is in the answer?

They lean on your site for facts and on other sites for judgement. In an Ahrefs test of 20,000 US prompts, official sites took 63.4% of ChatGPT citations on factual questions (pricing, features, company details) but only 46.4% on comparison questions 1. So your docs must carry the facts, while independent reviews, forums and articles will carry the comparisons.

Readable pages come first. Vercel found in late 2024 that none of the major AI crawlers it measured from OpenAI, Anthropic, Meta, ByteDance or Perplexity rendered JavaScript, while Gemini and Apple's crawler did 7. That study is old, so check your own site with the AI Crawler Checker. Docs sites built with client-side apps are the main risk.

OpenAI says it recommends allowing OAI-SearchBot in robots.txt, and that sites which opt out will not be shown in ChatGPT search answers 11. Our guide on making your site server-side rendered covers the fix.

What should an AI startup publish first?

Publish the pages that hold hard facts, and state limits as plainly as strengths. Start with a pricing page, a docs quickstart, a 'what it can and cannot do' page and one honest comparison per main rival. Write each answer first, then the detail. The method is in how to write comparison pages that get cited.

Docs deserve special care. Mintlify, a docs host, generates and hosts an llms.txt file (a Markdown index for AI tools) automatically and offers a Markdown version of each page 8. But Google says it does not use llms.txt, and an SE Ranking study of about 300,000 domains found no link between having one and AI citation frequency 612. Add it if it is free. Do not expect it to move results.

Do not mass-produce pages for every model name and use case. Google says creating separate pages for each query or fan-out variation mainly to manipulate rankings or AI answers violates its scaled content abuse policy 6.

Skeleton of a 'what it can and cannot do' page
  • Title: What [Product] can and cannot do (checked 20 Oct 2026)
  • Answer first: [Product] handles X and Y. It does not handle Z.
  • Tested on: [dataset or task], [date], [model version], [method link]
  • Result: [your own measured number, with the test setup]
  • Known limits: languages, context size, rate limits, data retention
  • Where a rival is better: [one honest row]
  • Corrections and changelog: [link], last reviewed on the date above

What rules apply to claims about AI products?

Marketing claims about AI are held to the same standards as any other claim. In September 2024 the US Federal Trade Commission announced Operation AI Comply, and the FTC's then-chair, Lina Khan, said 'there is no AI exemption from the laws on the books' 10. Its general policy requires a reasonable basis for objective claims before you publish them 13.

The US Securities and Exchange Commission has acted on 'AI washing' too. On 18 March 2024 it settled charges against two investment advisers, Delphia and Global Predictions, over false statements about their use of AI, with total penalties of $400,000 9. Those firms were advisers, so the case applies to them directly. The lesson for any startup is to describe only what the product does today.

This is general information, not legal advice. If your product touches finance, health or hiring, have counsel review capability claims, accuracy figures and customer logos before they go live.

How do you measure AI visibility for an AI startup?

Track a fixed prompt list monthly and score whether you are named, described correctly and linked. For an AI product, also check that the model name, version and price the engine states match your current docs, because those are the facts that go stale first. Run 10 to 15 prompts three times on two engines, and log the date and the model version of each engine. Answers vary between runs. Our guide to building a prompt list for tracking gives a template.

Add a free-text 'How did you hear about us?' field to every signup and demo form. Google recommends the Generative AI performance report in Search Console, which shows how your content performs in Google's generative AI features 6. AI feature traffic is also counted in the standard Performance report under the Web search type. Check which metrics the report shows for your property before you rely on it.

What have Chalk Labs and Rahil Jain done for AI startups, and what have they not?

Chalk Labs works in Web3 and AI, and its published client work in this field is press placements for AI and Web3 companies. Rahil Jain, a co-founder, builds AI agents and growth systems and runs Chalk Labs' AI SEO programme. We name no clients here.

Chalk Labs' published AI SEO cases are a Bengaluru trophy shop and a personal site. Neither reports AI-citation counts, and we have no published AI-citation results for AI startups. Chalk Labs sells this service, so weigh our view accordingly.

Your first 90 days

Days 1-30

  • Write 12 buyer and builder prompts and run each three times on two engines.
  • Check that docs, pricing and limits render without JavaScript and that OAI-SearchBot is allowed.
  • List every public capability claim and attach evidence to each.
  • Add a free-text source field to signup and demo forms.

Days 31-60

  • Publish the pricing page, a 'can and cannot do' page and a docs quickstart summary.
  • Publish two honest comparison pages for the rivals buyers name most.
  • Add 'last checked' dates and an owner to every fact page.

Days 61-90

  • Publish your own evaluation method with data others can cite.
  • Rerun the prompt log and compare it with day one.
  • Review Search Console's generative AI report.
  • Set a monthly refresh rhythm for pricing, model and comparison pages.

The first pages to publish

  1. Pricing and limits page in plain text. 'Price' appeared in 8.9% of ChatGPT fan-out queries in September 2026 1.
  2. What [Product] can and cannot do. Honest limits answer requirement prompts and protect you from overclaiming.
  3. Docs quickstart summary. Factual questions send 63.4% of ChatGPT citations to official sites 1.
  4. One honest comparison per main rival. Competitor prompts are 31% of first prompts 3.
  5. Integrations and languages page. Fit-with-my-stack is a common hidden question in category prompts.
  6. Your own evaluation method and results. Original data is the non-commodity content Google asks for 6.
  7. Changelog with model versions and dates. A dated record shows currency to people and crawlers.

Trust signals and risks

What AI looks for

  • Docs that render as plain text, with a quickstart that works.
  • A 'what it can and cannot do' page with your own dated test method.
  • Public pricing and limits, with a 'checked on' date.
  • A changelog and model-version history.
  • Named founders and engineers on technical pages.
  • Honest comparisons that include a row where a rival wins.

What can go wrong

  • Capability claims you cannot show, which regulators treat as deception 910.
  • Stale model names, prices and limits that AI keeps repeating.
  • Docs that need JavaScript to display, so crawlers may see an empty page 7.
  • Mass-produced pages for every use case and model, which Google may treat as scaled content abuse 6.

Why Rahil Jain for AI Startups?

Direct experience

Chalk Labs has done work in this field and can show it.

Chalk Labs works in Web3 and AI, with published press work for companies in this field, and Rahil Jain builds AI agents and growth systems. We have no published AI-citation results for AI startups. Its AI SEO cases are a trophy shop and a personal site.

Rahil Jain

Rahil Jain is an AI SEO (answer engine and generative engine optimisation) consultant and co-founder of Chalk Labs.

LinkedIn · Method and credentials

Results we can show

These are Chalk Labs' own search results. They are not a forecast for your site: results depend on your market, competition and starting point.

27.3k
Google clicks in 16 months for a local trophy shop in Bengaluru (Meru Trophy)
2.3M
search impressions over the same period, at an average position of 6.9
#1
ranking for “fractional PR manager crypto” within a month of launch (shilikajain.com, a low-competition niche phrase)

How to read these numbers

Chalk Labs reports them from Google Search Console data. We cannot publish the raw console. The shilikajain.com site belongs to Shilika Jain, a Chalk Labs co-founder, so treat it as a demonstration on our own network and not as an independent client result. Meru Trophy is a client of Chalk Labs.

See the case studies

Frequently asked questions

Does ChatGPT recommend AI startups?

It can name them, but nobody can promise it will name yours. The bigger risk for AI products is a wrong model name, version or price in the answer, because those change monthly. Check what engines say about your current model and fix the source page if it is stale. Answers vary by run, and we do not guarantee placement.

How often should an AI startup update its pages?

Update pricing, model, limits and comparison pages at least monthly and whenever something changes. The median page lost half its AI citation share within 11 days of peaking, and sustained pages had a median half-life of 27 to 29 days 2. Show a 'last checked' date on each page.

Do AI startups need an llms.txt file?

It is optional. Google says it does not use llms.txt and that having one neither helps nor harms Google visibility 6. SE Ranking's study of about 300,000 domains found no link between having one and AI citation frequency 12. If your docs host creates one automatically, as Mintlify does, leave it on 8.

Can I say my product uses AI if it only uses a simple model?

Only if the claim is true and you can show it. US regulators have acted on false AI claims, including the SEC's March 2024 cases against two investment advisers 9 and the FTC's Operation AI Comply 10. Describe what the product actually does today, and keep the evidence on file.

What is the first page an AI startup should publish?

A plain-text pricing and limits page with a 'checked on' date. 'Price' appeared in 8.9% of ChatGPT fan-out queries in September 2026 1, and buyers cannot get your price from a source you do not control. Then add a 'can and cannot do' page and one honest rival comparison.

Sources

  1. How ChatGPT Does Query Fan-Out (15 Million Queries Studied) · AhrefsOpened October 20, 2026. Supports: Article dated 12 October 2026: 66.6 million free, logged-out ChatGPT responses (August and September 2026); September modifiers 2026 at 22.9%, official 13.1%, price 8.9%, best 5.3%; separate 20,000-prompt test: official sites took 63.4% of citations on factual questions and 46.4% on comparison questions.
  2. The half-life of an AI citation is 11 days · ProfoundOpened October 20, 2026. Supports: Published 30 September 2026: 883,000 pages across seven answer engines; the median page lost half its citation share within 11 days of peaking; sustained pages had a median half-life of 27 to 29 days; comparison, listicle and review pages 29 days; 6.8% of pages cited by five or more engines still held above half-peak at day 56 against 1.2% for one engine. Profound sells AI visibility tools.
  3. G2's 2026 AI Search Insight Report · G2Opened October 20, 2026. Supports: March 2026 survey of 1,076 B2B decision-makers: 51% start software research with an AI chatbot more often than Google; 69% were led to a different vendor than planned; first-prompt types 33% category, 31% competitor, 22% requirements, 9% ecosystem, 6% budget. G2 sells review and marketing services.
  4. Gartner Survey Finds 69% of B2B Buyers Turn to Sales Reps to Validate AI-Generated Insights · GartnerOpened October 20, 2026. Supports: Press release of 20 May 2026: survey of 645 B2B buyers (August to September 2025); 45% used generative AI in a recent purchase, mainly to gather vendor information; an average of seven information sources; 69% prefer to validate AI-generated insights with sales reps.
  5. 2025 Stack Overflow Developer Survey: AI · Stack OverflowOpened October 20, 2026. Supports: 84% use or plan to use AI tools, 47.1% daily; 26.1% somewhat and 19.6% highly distrust AI accuracy; 66% name almost-right answers as the top frustration; Ollama (51.1%) and LangChain (32.9%) were the most used orchestration tools.
  6. Optimizing your website for generative AI features on Google Search · Google Search CentralOpened October 20, 2026. Supports: Last updated 10 July 2026: no special markup needed; Google does not use llms.txt, which neither helps nor harms; separate pages per query or fan-out variation to manipulate results violate the scaled content abuse policy; non-commodity content offers unique expert or experienced takes; recommends the Generative AI performance report in Search Console. A page must be indexed and eligible to show with a snippet (stated on Google's AI features page, https://developers.google.com/search/docs/appearance/ai-features, which we also opened).
  7. The rise of the AI crawler · VercelOpened October 20, 2026. Supports: Article dated 17 December 2024: OpenAI, Anthropic, Meta, ByteDance and Perplexity crawlers did not render JavaScript; Gemini and AppleBot did; advises server-side rendering for critical content. Older data; behaviour may have changed.
  8. llms.txt (Mintlify documentation) · MintlifyOpened October 20, 2026. Supports: Mintlify automatically generates and hosts llms.txt and llms-full.txt for docs and adds .md versions of pages.
  9. SEC Charges Two Investment Advisers with Making False and Misleading Statements About Their Use of Artificial Intelligence · U.S. Securities and Exchange CommissionOpened October 20, 2026. Supports: 18 March 2024: Delphia and Global Predictions settled over false AI claims; total penalties $400,000. Both were investment advisers.
  10. FTC Announces Crackdown on Deceptive AI Claims and Schemes · U.S. Federal Trade CommissionOpened October 20, 2026. Supports: 25 September 2024: Operation AI Comply; chair's statement that there is no AI exemption from the laws on the books.
  11. Overview of OpenAI Crawlers (bots) · OpenAIOpened October 20, 2026. Supports: OAI-SearchBot surfaces sites in ChatGPT search; OpenAI recommends allowing it in robots.txt; sites that opt out will not be shown in ChatGPT search answers.
  12. LLMs.txt study · SE RankingOpened October 20, 2026. Supports: Article dated 15 October 2026: roughly 300,000 domains, 10.13% with llms.txt, and no correlation found with AI citation frequency.
  13. FTC Policy Statement Regarding Advertising Substantiation · U.S. Federal Trade CommissionOpened October 20, 2026. Supports: Dated 23 November 1984: advertisers must have a reasonable basis for objective claims before disseminating them; lacking one is an unfair or deceptive practice under Section 5.
How this page was made. Built on Rahil Jain's AI SEO method and drafted with AI research assistance. Every statistic links to the page we read it on (opened October 20, 2026 or nearby). Published October 20, 2026; last updated October 20, 2026. Chalk Labs sells AI SEO services, so we have a commercial interest in this topic. Spotted an error? Tell us and we will fix it.