What is AI search visibility and why does it matter?

AI search visibility is the degree to which an AI system; such as ChatGPT, Perplexity, Claude or Google Gemini Overviews; can parse, trust and cite your website content in generated answers. A page that scores poorly for AI search visibility is unlikely to appear as a source or citation in AI generated responses, regardless of its traditional search ranking. AI search visibility depends on structured data quality, heading clarity, content depth, metadata completeness and machine readable formatting.

How does AiVIS.biz calculate a search visibility score?

AiVIS.biz runs a multi-stage audit pipeline: it crawls the target URL with a real browser, extracts structural signals (headings, schema, meta tags, body content, links), runs technical checks, then passes the evidence to a primary AI model for scoring. On Signal and Score Fix tiers, a second AI model peer-critiques the findings and a third validates the final result. The 0-100 CITE LEDGER score is a weighted composite across seven dimensions: Schema and Structured Data (20%), Content Depth (18%), Technical Trust (15%), Meta Tags and Open Graph (15%), AI Readability (12%), Heading Structure (10%), and Security and Trust (10%). Each finding is tied back to real page evidence.

What is the difference between AiVIS.biz and traditional query and answer engine tools?

Traditional SEO tools optimize for search engine crawlers and ranking signals like backlinks or keyword density. AiVIS.biz focuses specifically on answer engine readiness; whether AI systems can extract, trust and cite your content in generated responses. AiVIS.biz evaluates machine readability, entity clarity, schema coverage and citability rather than link authority. Every recommendation is evidence-linked, meaning it references the specific crawled signal that triggered it, not a generic checklist. The platform also tracks brand presence across citation sources and discussion platforms: Answer Engine Optimization (AEO).

What sources does brand mention tracking monitor?

Brand mention tracking scans 19 public sources: Reddit, Hacker News, Mastodon, DuckDuckGo site search, Bing site search, Google News RSS, GitHub repositories, Quora, Product Hunt, Stack Overflow, Wikipedia, Dev.to, Medium, YouTube, Lobsters, Bluesky, Twitter/X, Lemmy and GitHub Discussions. Available on the Alignment tier and above.

What plans does AiVIS.biz offer and what is included?

AiVIS.biz offers five tiers. Observer (free) includes 3 scans per month powered by open-weight AI models. Starter ($15/month) includes 15 scans per month with full recommendations, implementation code, PDF exports, and shareable links. Alignment (Core, $49/month) includes 60 scans per month with paid AI models, competitor tracking, brand mention scanning, reverse engineering tools, and WebMCP access. Signal (Pro, $149/month) includes 200 scans per month with a triple-check pipeline, citation testing, niche ranking, scheduled citation tests, team workspaces with 25 seats, full MCP/API access, and white-label reports. Score Fix (AutoFix PR, $299 one-time) uses higher capacity models and generates automated GitHub PRs with 250 credits.

How to Fix AI Extraction Failures | AiVIS.biz

An extraction failure is any point where the AI pipeline breaks between accessing your page and reproducing your content in an answer. Each failure type has a specific structural fix.

Types of extraction failure

Access failure: AI crawlers are blocked by robots.txt, WAF, CDN, or rate limiting. The crawler never sees your content. Fix: update robots.txt, whitelist AI crawlers, configure CDN bot protection.

Rendering failure: Your page uses client-side rendering. The crawler receives empty HTML. Fix: enable SSR, SSG, or pre-rendering.

Parsing failure: Content exists but lacks structural markers. No headings, no schema, no clear segmentation. The model extracts but cannot organize the content. Fix: add semantic HTML, heading hierarchy, JSON-LD.

Attribution failure: Content is extracted but cannot be attributed. Missing Organization schema, no author, no dates. Fix: add complete entity metadata.

Prioritizing fixes by impact

Access failures block everything downstream — fix them first. Rendering failures have the second-highest impact since they eliminate all content delivery. Parsing and attribution failures are fixable incrementally and typically produce measurable score improvements within one audit cycle.

Using AiVIS.biz to fix extraction failures

Run an audit. Each finding includes a BRAG Evidence ID linking to the specific extraction failure. Starter tier and above provides implementation code for each fix. Score Fix (one-time purchase) automates the fixes as GitHub pull requests.

Frequently Asked Questions

What percentage of sites have AI extraction failures?: Based on AiVIS.biz audit data, the majority of sites have at least one significant extraction failure. Missing structured data, blocked AI crawlers, and client-side rendering are the most common.
Can Score Fix automatically fix extraction failures?: Score Fix translates audit findings into code-level changes and ships them as GitHub pull requests. It covers schema additions, metadata fixes, heading corrections, robots.txt updates, and llms.txt generation.