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How Everfound works

The people looking for what you do increasingly ask AI assistants instead of searching and clicking. Those assistants answer from what they can read on your site and what third-party sources say about you. Everfound measures both sides and hands you the fixes. It works in seven steps:

  1. 01

    We read your site

    We read your pages, plus the machine files AI crawlers use: robots.txt, sitemap.xml, llms.txt, and your structured data.

  2. 02

    We ask real discovery questions

    We ask the same kinds of questions your potential customers ask, across all six AI assistants.

  3. 03

    We measure who appears

    Every answer is recorded: whether you were mentioned, where you ranked, and which competitors were recommended instead.

  4. 04

    We diagnose why

    Each miss is traced back to the missing or unclear signal that caused it, with the evidence shown.

  5. 05

    We generate the fixes

    You get a ranked action plan with a ready-to-use implementation prompt for each change, filled in with your site's real details.

  6. 06

    We measure what moved

    When your fixes are live, run the audit again: Everfound re-crawls your pages, re-scores every check, and shows the ones that now pass, the ones that improved, and the ones that lost ground since your last run.

  7. 07

    We keep watching

    Weekly monitoring re-asks up to 3 of the questions you track across all 6 AI assistants, watches your pages for changes, and emails you when something moves: a lost mention, your share of mentions slipping, or an assistant still repeating something you took down.

The method in detail

The full methodology, for anyone evaluating the measurement itself. Every claim here stays true to the implementation.

The crawl

We fetch your homepage and up to 30 pages (your selection, or our auto-pick of the pages that matter: pricing, FAQ, about, comparisons), plus the machine files AI crawlers use: robots.txt, sitemap.xml, and llms.txt. We parse any JSON-LD structured data, and we report coverage honestly: “audited X of N pages found.”

The live visibility test

We take the questions real people type when they need what you offer and ask them across six AI engines: OpenAI GPT-5.5, Anthropic Claude Opus 4.8, xAI Grok 4.3, Google Gemini 3.5 Flash, and Perplexity Sonar Pro with live web search, plus Google AI Overviews read from live Google results. We ask each engine every question once per run, and read the aggregate across questions, engines, and runs rather than any single answer. For every answer we record whether you were mentioned, where you ranked, how you were framed, and which sources the engine cited. The report keeps the receipts: the actual answers and the exact links, so nothing here is our word against the machine's.

The diagnosis and the implementation kit

Findings and the action plan are grounded in what the crawl actually saw. The kit is evidence-aware: anything your site already does well (existing schema, an existing llms.txt, an existing FAQ) is acknowledged and extended, never replaced. Intentionally built content stays yours. We suggest merges, not rewrites.

What the score is based on

The score out of 100 weighs six categories:

  • Entity clarity (20%) · can a machine tell what you are, who you serve, and why it should recommend you.
  • Crawlability (18%) · can AI crawlers reach and read your pages, including robots.txt, sitemap.xml, and llms.txt.
  • Structured data (18%) · the JSON-LD that fits your kind of site.
  • Owned content (16%) · the pages assistants quote: FAQ, pricing, about, comparisons.
  • AI answer presence (16%) · the live measurement above.
  • External consensus (12%) · the third-party footprint AI trusts.

Three properties keep the score honest. Every check shows its evidence: the report tells you why each point was earned or missed, never just a number. The on-page half is deterministic: the same site with the same facts scores the same, every time. And checks only apply where they're fair: a law firm isn't graded on app-store presence, a web-only product isn't penalized for skipping Google Play, and a check we didn't run (like llms.txt on an older crawl) is omitted rather than counted against you.

Why AI results vary between runs

The on-page half of your score is stable. The live half measures a moving world: AI engines sample their answers, their web indexes shift daily, and the same question can surface different sources on different days. A mention count that reads 5/17 one day and 4/16 the next is normal variance, not a regression. It usually means one engine answered differently, or one call returned nothing (we drop failed calls rather than invent them). That's why we show the receipts and judge the trend across runs rather than any single point.

What to expect from a re-audit

A re-audit re-crawls your site fresh, re-asks the same questions, and appends a new run to your history. It never overwrites anything. On-page fixes register immediately: publish schema, add missing pages, fix readability, and the deterministic categories move the same day. The report then shows the comparison, check by check: the ones that now pass, the ones that improved, and the ones that lost ground. Every previous run stays retrievable, so the before and after is provable, not anecdotal.

  • Same re-audit: structured data, llms.txt, new pages, content fixes, entity clarity. The crawl sees them and the score moves immediately.
  • Days to weeks: AI mentions, rankings, and citations move on the engines' timetable, not yours. They re-crawl and re-index on their own schedule. The repairs make you recommendable; the engines decide when to notice. This lag is real and we won't pretend otherwise.
  • Compounding: the “Where AI looks” list in your report names the third-party sources the engines actually cited for your category. Earning presence there is the long game that moves the live half.
Monitoring

Because the live half moves on the engines' schedule, monitoring keeps measuring it for you. Weekly monitoring re-asks up to 3 of the questions you track across all 6 AI assistants, watches your pages for changes, and emails you when something moves: a lost mention, your share of mentions slipping, or an assistant still repeating something you took down. It does not re-crawl or re-score your site. That is what a re-audit does, so the on-page half of your score moves when you run one, not on the weekly schedule. Monitoring is priced per site and runs on a plan. A one-time audit measures a single point in time and includes a second run to check your fixes worked. Cancel anytime. The weekly runs build the trend lines that turn one-off measurements into proof.

How you know it's working

We designed the audit so you never have to take our word for anything. The visibility section links the actual AI answers and the exact pages they cited. Every score check shows its evidence. Every run is kept, so improvement is a measured delta, not a promise. The honest claim is this: we repair the inputs AI systems act on (readability, clarity, structure, citability), then measure weekly whether the engines respond. When the mentions come, you'll see exactly where, in which engine, framed how, and against which competitors. If something we recommended didn't move the needle, the same receipts will show that too.