SEO, AEO and GEO for AI Startups

SEO for an AI startup starts with the security review

The first question is almost always about data, and it is really three questions with three different answers. OpenAI answers all three in its own documentation. On training, its per-endpoint table reads No in the "Data used for training" column for every listed API endpoint. On retention, it states that "By default, abuse monitoring logs are generated for all API feature usage and retained for up to 30 days, unless longer retention is required by law, or is reasonably necessary to protect our services or any third party from harm." On residency, it states that "Data residency does not apply to system data, which may be processed and stored outside the selected region." Read the OpenAI data controls documentation and then write the same three answers for your own product.

A page that answers only the training question fails the review. So does a page that promises residency without naming the exception.

Governance is the second question, and the framework a buyer names is usually the NIST AI Risk Management Framework. NIST is explicit that it "is intended for voluntary use and to improve the ability to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems." It is a voluntary framework and it is not a law. Its core runs on four functions: Govern, Map, Measure, and Manage. Say which of the four you can evidence today. Read NIST's own page before you write that section.

If you provide a general-purpose model into the European market, the obligations are already live. The European Commission states that "The AI Act rules on GPAI became effective in August 2025", and that "The transparency rules of the AI Act will come into effect in August 2026." The first named provider obligation in Article 53 is a document: providers must "draw up and keep up-to-date the technical documentation of the model, including its training and testing process and the results of its evaluation". If you hold that document, the page should say so.

Then there is the badge problem. SOC is an assurance service, and the AICPA defines it as "a suite of service offerings CPAs may provide in connection with system-level controls of a service organization or entity-level controls of other organizations." The standard-setter is now warning about its own signal, carrying the headline "Promises of 'fast and easy' threaten SOC credibility" on that page. A logo answers less than it once did. The fix is to offer the report and state its scope.

The last one catches every startup built on someone else's model. Microsoft describes its Customer Copyright Commitment as "a provision in the Microsoft Product Terms that describes Microsoft's obligation to defend customers against certain third-party intellectual property claims relating to Output Content", and conditions it on named mitigations the customer carries, starting with the rule that "The customer offering must include a metaprompt directing the model to prevent copyright infringement in its output". Google Cloud scopes its own indemnity to an enumerated service list. You inherit a conditional, service-scoped indemnity. Your buyer will ask what you indemnify. That belongs on a page.

Training and answer eligibility are separate switches

Blocking AI crawlers is an avoidable mistake on an AI startup's website, and it usually comes from one confusion. Every platform runs separate agents for separate jobs, and only some of them decide whether you appear in an answer.

OpenAI names the one that matters: "OAI-SearchBot is used to surface websites in search results in ChatGPT's search features", and "Sites that are opted out of OAI-SearchBot will not be shown in ChatGPT search answers". It also states the split plainly, that a webmaster "can allow OAI-SearchBot in order to appear in search results while disallowing GPTBot to indicate that crawled content should not be used for training OpenAI's generative AI foundation models". Read the OpenAI bots documentation with your infrastructure owner.

Perplexity draws the same line: "PerplexityBot is designed to surface and link websites in search results on Perplexity. It is not used to crawl content for AI foundation models." Anthropic states the cost of getting it wrong: "Disabling Claude-SearchBot on your site prevents our system from indexing your content for search optimization, which may reduce your site's visibility and accuracy in user search results." And Google separates the two completely, stating that "Google-Extended does not impact a site's inclusion in Google Search nor is it used as a ranking signal in Google Search." Google-Extended governs Gemini training and grounding. It is not a Search switch.

There is a second failure that no robots.txt file will reveal. Perplexity states that "If you're using a Web Application Firewall (WAF) to protect your site, you may need to explicitly whitelist Perplexity's bots to ensure they can access your content". A bot allowed in robots.txt and blocked at the edge looks identical to a bot that was never invited. The highest-fidelity way to know which agent actually arrived is the server log, which is why we read them where the hosting allows it. Logs and probability-based visibility tools complement each other, so we use both. Our log-file playbook sets out the method.

Google, for its part, publishes no separate entry ticket. It states that "There are no additional requirements to appear in AI Overviews or AI Mode, nor other special optimizations necessary", that a page must be "indexed and eligible to be shown in Google Search with a snippet", and that "There's also no special schema.org structured data that you need to add." What it does name is access and text: crawling allowed in robots.txt "and by any CDN or hosting infrastructure", and "important content is available in textual form". Those two items describe many AI startup websites. Read Google's AI features documentation and check both.

One more mechanic changes how many pages you need. Google documents that its AI surfaces may issue "multiple related searches across subtopics and data sources" to build one response. One long page does not answer a fan-out. A set of pages that each resolve one question does.

Publish the accuracy evidence a developer will check

The most useful number for an AI startup selling to engineers is a trust statistic. Stack Overflow's 2025 Developer Survey reports that "84% of respondents are using or planning to use AI tools in their development process", and, in the same survey, that "More developers actively distrust the accuracy of AI tools (46%) than trust it (33%)". Positive sentiment fell from over 70% in 2023 and 2024 to 60% in 2025. The survey states its own scope: "49,009 responses from 177 countries are used in these survey results."

Read that pairing carefully. Engineers already accept that AI tools work. They need to know yours is accurate enough to be accountable for. Accuracy evidence is the conversion asset.

So publish the eval. OpenAI defines evals as tests that "test model outputs to ensure they meet style and content criteria that you specify", run "especially when upgrading or trying new models". An eval claim without a dataset and a grader is the AI equivalent of research-backed with no study. Name the dataset, name the grader, name the threshold you ship at.

The same rule governs your documentation surface. Hugging Face defines the model card as the README of a model repository and says what it should describe: the model, "its intended uses & potential limitations, including biases and ethical considerations", the training parameters, the datasets used to train the model, and "the model's evaluation results". It also notes that the YAML metadata block in that README supports discovery of the model. GitHub says the same thing about repositories: "A README is often the first item a visitor will see when visiting your repository", and topics exist so "other people find and contribute to your project". For an AI startup, the model card and the README are search assets.

Name the company so an engine can tell it apart

An AI startup named after a common noun is competing with the dictionary. Google documents the fix. Organization structured data on the home page can help Google understand the organization, and one of the disambiguation properties is sameAs, which Google defines as "The URL of a page on another website with additional information about your organization". Pair that with one category sentence repeated on every surface you control. An engine can only repeat language it can find. Our guide to entity disambiguation covers the work in full.

Comparison pages are the other surface you own outright. The review sites run on algorithms: G2 states that "A software's G2 Score is calculated using two scoring components: Satisfaction and Market Presence", that "Because recent reviews are more relevant to buyers, older reviews are weighted less", and that its reviews "do not constitute expert opinions based on objective criteria". You cannot edit that. You can publish your own comparison and alternatives pages, and a buyer choosing between two products will read them.

Start with the pages your security questionnaire keeps asking for

The fastest useful backlog is already in your sales inbox. Look at what your team attaches after a demo, and at the questions that reappear in every security review. Those attachments should be indexed pages.

Then decide which pages need to exist first:

1. A category page that states what the product is, who uses it, and what it replaces.
2. A data-handling page that separates training, retention and residency, each with its control.
3. A governance page that names the framework you map to and the documentation you hold.
4. An accuracy page that publishes the eval, the dataset and the grader.
5. A model card, a README and a quickstart an engineer can finish without a call.

If you are comparing agencies, read our guide to the best SEO and AEO agencies for AI startups.

LoudFace scopes the work around your category, your buyer, your current site and your commercial goals. Start with an AI visibility audit when you need your AI search presence scored against your competitors, with one fix you can implement within a week. Engagements start from $5k/mo, and the pricing page explains the Solo, Dual, and Scale plan structure.