HOW IT WORKS
How the AI Search Readiness Audit Works
Three steps. Six pillars. One report.
Answer Patch scores how ready your site is for AI search engines and hands back the exact edits to close the gaps. Here is how the process works, what each pillar measures, and what a result looks like.
01 / SCAN
Crawl what matters.
Submit a domain and Answer Patch crawls the homepage along with a handful of high-intent pages — service pages, about, contact, FAQ, and whatever else the site actually has. It also fetches robots.txt and the sitemap, pulling together evidence about how AI systems currently see the business: what it is called, what it does, where it operates, what proof of trust it shows, and what a crawler can and cannot reach.
The scan is free, takes about two minutes, and needs no account, no email, and no credit card. You get a scored result before anyone asks for money.
02 / SCORE
Six-pillar audit.
The evidence feeds a deterministic rules engine — the same site produces the same baseline findings every time, because no AI model is involved in scoring. Findings roll up into six pillars of AI search readiness, each with its own 0-100 score, and every finding links back to the exact page, element, or robots rule that produced it.
Scores of 80-100 are rated Strong — most audited signals are clear, fetchable, and answer-ready. Scores of 60-79 are Improving — useful foundations with meaningful gaps remaining. Anything under 60 is Needs Work — important crawlability, clarity, schema, or proof signals are missing.
03 / FIX
Ready-to-paste edits.
Buying the $19 Fix Report unlocks up to ten prioritized fixes, ranked by impact and effort, with copy-ready snippets for the ones AI assistance can safely draft — title tags, meta descriptions, FAQ copy, JSON-LD — and clear instructions for the ones that need a human decision, like pricing, licensing claims, or regulated wording.
The report is emailed and stays viewable at a private, token-protected share link. There is no subscription — a Fix Report is a single $19 purchase per audit, and nothing recurs.
THE AUDIT
The Six Audit Pillars
Business Clarity
Checks whether the core facts of the business — what it does, what it is called, what it sells, who it serves, where it operates — are stated in plain, unambiguous language on the pages that matter, not just implied by design or buried in an image. An AI system that has to guess what a business does from context clues is an AI system that under-cites that business.
Learn more →Answer Readiness
Checks whether content is structured so a passage can be lifted and quoted as a direct, well-supported answer: clear headings, FAQ-style question-and-answer blocks, and copy that leads with the answer instead of building up to it. This is what Answer Patch calls “citability” — how easily a single passage stands on its own as a citable answer.
Learn more →Technical Access
Checks whether AI crawlers can actually reach and read the site: robots.txt rules, response codes, redirect chains, and whether JavaScript-only rendering hides content a non-browser fetch cannot see. It also checks for the presence and quality of an llms.txt file as an emerging signal that a site has deliberately made itself legible to AI systems.
Learn more →Structured Data
Checks whether the site publishes valid JSON-LD — Organization, LocalBusiness, FAQPage, BreadcrumbList, Product, and similar schema.org types — that lets a machine verify entities and facts instead of inferring them from prose. Schema that is present but malformed, or schema that contradicts the visible page content, counts against this pillar just as much as schema that is missing outright.
Learn more →Trust Signals
Checks for the proof AI systems are known to weigh before treating a source as citable: reviews, credentials, years in business, authorship, physical address, and other experience-and-expertise signals. This is Answer Patch's version of E-E-A-T for an AI-search context, not just a traditional-SEO trust checklist.
Learn more →Competitive Visibility
Compares the site's readiness against other sites already being cited in its category, so a score is not just “good in isolation” — it is good, or not, relative to the competitors an AI system is actually choosing between. Findings in this pillar come with a specific comparison and a specific action, not just a number.
Learn more →EXAMPLE
What a Scan Looks Like
| Pillar | Score | Finding |
|---|---|---|
| Business Clarity | 72 | Strong on what the business does, weak on where it operates |
| Answer Readiness | 41 | No FAQ blocks; headings don't map to real questions |
| Technical Access | 65 | robots.txt allows AI crawlers; no llms.txt found |
| Structured Data | 30 | No JSON-LD detected on any crawled page |
| Trust Signals | 55 | Reviews present; no credentials or years-in-business shown |
| Competitive Visibility | 44 | Two category competitors already publish FAQ schema |
Overall: 58 / 100 — Needs work
Illustrative example — not a real business or scan result.