By Tangenix · Published 22 October 2026

AI search visibility checklist for app founders

To make an app discoverable in AI search, publish accurate product information on accessible public pages, make those pages easy to discover and give readers useful evidence. Then measure whether specific answers cite them. Crawl permission, a sitemap submission and a structured-data test are intermediate checks; none proves that an AI system recommends your app.

Start with a product facts page

Explain who the app serves, the task it performs, its availability and any confirmed limitations. Show pricing only when you have announced it. Link to documentation, contact information and relevant examples. If capabilities are planned, say so beside the claim. Consistent facts help readers distinguish a working capability from a roadmap promise.

For Crany, the public positioning is an AI marketing co-founder for vibe coders and software founders, designed to strategize, execute approved marketing and improve based on results. Crany is in development and the public waitlist is open. Public launch timing and paid-plan pricing have not been announced. This is a product description, not an independent recommendation.

Separate search crawling from model training

OpenAI documents OAI-SearchBot as its search crawler and GPTBot as a separate control for model training. Allowing a training crawler is not a prerequisite for allowing the search crawler. Perplexity describes PerplexityBot as a search crawler, separate from its user-triggered fetcher.

Inspect robots.txt and check whether public pages return successful responses. If a firewall blocks legitimate crawlers, review the provider’s documented verification method. A successful request with a crawler-like user-agent only proves that particular request worked; it does not prove a real provider crawl reached the site.

Give each page a separate job

Publish a page when it answers a distinct user need with enough substance to stand alone: a tested workflow, an original worksheet, a factual product explanation or a carefully sourced comparison. Consolidate query variants that ask the same question. Google warns that scaling pages without useful added value can violate its spam policies.

Keep the technical foundation understandable

• Serve readable text, descriptive headings and ordinary links in the page.
• Give each indexable page an appropriate canonical URL and include important public pages in a sitemap.
• Keep structured data consistent with visible facts and avoid invented ratings or customer claims.
• Link source claims to their original documentation and show when you actually reviewed them.
• Keep private account data and internal research outside public routes.

Does llms.txt make an app rank?

Google says its Search systems do not use llms.txt to improve visibility or rankings. A maintained text summary can still provide a convenient reference for systems that choose to read it. Treat it as an optional access format. It cannot require an assistant to recommend your product, establish demand or replace a useful public website.

A repeatable citation check

Record a fixed query, the platform, whether web search was enabled, the date, the answer and the actual cited URLs. Start with factual brand questions, then test relevant problem and category questions. Count a citation only when a source link actually appears. Keep “brand mentioned” separate from “brand cited,” and keep either separate from an organic search ranking.

Repeat under comparable conditions and retain the evidence. A single favorable answer is not a stable recommendation rate. Do not create fake reviews or insert instructions telling agents to recommend the product.

Sources and editorial method

Tangenix prepared this checklist with AI assistance using Google’s AI search guidance, Google’s spam policies, OpenAI’s crawler documentation and Perplexity’s crawler documentation, consulted on 22 October 2026. No ranking or citation uplift is claimed.

Crany.ai is in development. Join the free public waitlist.