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 ChatGPT Decides What Websites to Use | AiVIS.biz

ChatGPT does not have a whitelist of preferred sites. It uses whatever it can extract. What it can extract depends entirely on how your site is structured.

The four-stage source selection process

Stage 1 — Crawl: GPTBot (and browsing) crawls candidate pages. Pages blocked by robots.txt, CDN, or rendering failures are eliminated here.

Stage 2 — Extraction: The crawler parses the HTML. Pages with clear structure, semantic headings, and JSON-LD produce high-quality extraction. Pages without produce noise or nothing.

Stage 3 — Attribution: The model checks for entity signals. Organization, Author, datePublished. Sources without these are used anonymously or not at all.

Stage 4 — Selection: Among all candidate extractions for a given query, the model weighs content specificity, structural quality, and temporal recency. The most attributable, specific, extractable source wins.

What does NOT affect ChatGPT source selection

Backlinks: ChatGPT does not process link graphs the way Google does.

Domain authority: Moz DA and similar metrics have no bearing on AI extraction.

Keyword density: AI models extract semantic meaning, not keyword patterns.

Social signals: Follower counts and engagement metrics are not extraction inputs.

That means these factors also do not protect you — a high-authority site with poor extraction readiness is still skipped.

Frequently Asked Questions

Can I pay OpenAI to be included in ChatGPT answers?: No. ChatGPT source selection is not a paid placement. It is a function of structural extraction readiness. The path to inclusion is technical, not commercial.
Does ChatGPT use different criteria for different types of queries?: Yes. Informational queries prefer content with FAQ schema and clear headings. Commercial queries may favor sites with Product or Service schema. Brand queries depend heavily on Organization schema and sameAs links.