About AnswerRank
A free tool that tells you whether AI answer engines can read, understand and cite your website — and exactly what to fix if they can't.
Why this exists
Search is splitting in two. People still type queries into Google, but a growing share now ask ChatGPT, Perplexity or Gemini and read a single synthesised answer instead of scrolling ten blue links. In that answer, only a handful of sources ever get named.
Traditional SEO tools were built for the blue-links world. They check title tags, meta descriptions, backlinks and page speed — all still useful, none of which tell you whether a language model can actually parse your page and quote it. That gap is what AnswerRank measures.
What the audit does
Enter a URL and the engine fetches the page exactly as a crawler would, then scores 19 AI-visibility signals and 12 technical health checks. The two are scored separately and shown side by side, deliberately — a site can be technically flawless and still invisible to AI, and averaging the two would hide precisely that.
The signals fall into four groups:
- Can a crawler reach you? robots.txt rules for GPTBot, ClaudeBot, PerplexityBot, Google-Extended, CCBot and others.
- Can it read you? Whether your content exists in the served HTML or only appears after JavaScript runs.
- Can it understand you? Structured data, entity clarity, headings, semantic HTML, author signals.
- Can it quote you? Whether content is shaped into self-contained, liftable answers.
It also asks a live language model what it already knows about your domain. That single question is often more revealing than the score — plenty of sites with tidy markup get back "I have no reliable information about this website."
How it's built
The audit engine is plain JavaScript with no third-party SEO API behind it. Every signal is computed from the page we fetch, the robots.txt we parse, and the sitemap the site publishes. Scoring logic lives in tested modules — the geometry of the charts, the ranking of fixes and the search over checks all have unit tests, because a tool that reports numbers has no business guessing them.
Fix suggestions are generated by a language model, but their priority is not. Severity is computed arithmetically from each signal's weight and how badly it failed, then it overrides whatever the model suggested. An earlier version let the model rank its own advice and it once labelled a fully blocked AI crawler as "low priority" — on a tool whose entire purpose is AI visibility.
Who built it
AnswerRank is built and maintained by Sastihari S, a full-stack and AI developer working under the name LUCIFER, based in Tamil Nadu, India. It started as an internal checklist used on client projects and became a public tool because the checklist kept finding the same problems on site after site.
It is free, needs no sign-up, and stores nothing about the sites you audit. If you want the fixes implemented rather than just listed, or you need something similar built, get in touch.
Limits worth stating
The audit reads one page at a time, not your whole site. It reports what the served HTML contains, which is the right measure for AI crawlers but not identical to what a human sees in a browser. And no tool can promise you a citation — AI engines weigh authority, freshness and topical fit in ways nobody outside those companies can measure. What this tool can do is remove every mechanical reason for you to be skipped.