Questions people ask before they scan

Lantad is an AI-visibility scanner. You paste a URL and it reads the page the way an AI crawler does, with no browser and no JavaScript, then reports whether crawlers such as GPTBot and ClaudeBot can reach it, whether your words survive the fetch, and whether its structured data is usable. It grades the result into an AI Visibility Score and withholds that grade on any page it could not measure.

Four things, weighted and combined: access, whether an AI crawler can fetch the page at all; prose parity, whether the crawler sees the same words a browser does; structure, whether the content is organised so a passage can be extracted; and schema, whether there is structured data that matches the page. The exact weights are published on the methodology page, and a scan that could not measure a component says so rather than guessing.

A single-page scan is free. Paid plans and done-for-you services cover whole-site audits, repeated monitoring, and the work of fixing what a scan finds. The current plans are on the pricing page.

A rank tracker tells you your position in a list of blue links. Lantad tells you whether an AI answer engine can read and cite your page at all, which is a different and earlier question. Ranking first in Google no longer guarantees you appear in an AI Overview or get named by ChatGPT, because those answers are built from pages the engine could actually fetch and parse.

It tests the named crawler tokens the major engines publish, including GPTBot and OAI-SearchBot from OpenAI, ClaudeBot from Anthropic, PerplexityBot from Perplexity, and Google-Extended and Googlebot from Google, among others. The full list, with what each token is for, is in the AI crawler directory and on the crawler conduct page.

It can, if you block the wrong thing. Google-Extended governs Gemini training and does not affect Google Search, but blocking Googlebot to keep AI out also removes you from Google Search. Lantad shows which bots each page actually allows, so you can separate the crawler you may want to block from the one you cannot afford to.

A page an AI crawler can fetch, that serves its words in the HTML without needing JavaScript, and that carries structured data matching its content will score well. Rather than chase a single number, read the report: it names the specific defect behind any sub-score, so a low score is a list of things to fix rather than a verdict.

There is no measured evidence that it does. Lantad ships a tool to check llms.txt and has published its own finding that the file has no measured effect on the engines that matter. It is cheap to add and may help discovery, but it is not a ranking factor, and this site says so rather than selling it.

Yes. The report is scoped so an engineer can act on it directly, and the done-for-you services implement the fixes when you would rather not. Either way the work starts from a measured finding rather than a discovery call.