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.

Why AI Attributes My Content to Competitors | AiVIS.biz

You wrote the content. A competitor gets the citation. This happens when your entity identity is weaker than your competitor's — and AI models default to the source with clearer attribution signals.

Attribution is a competition

When multiple sources cover the same topic, AI models must decide which source to cite. The model evaluates extraction quality, entity clarity, and structural reliability. If your competitor has Organization schema, dated content, and clear authorship while you have none, the model attributes the shared knowledge to them.

This is not plagiarism by AI. It is competitive extraction — the source with stronger signals wins the attribution.

Signals that determine attribution priority

Organization schema with sameAs links to verified profiles. datePublished property — earlier publication dates can establish priority. Author Person schema with credentials. Domain-level trust signals (HTTPS, consistent canonical URLs). Content specificity — more specific claims are harder to attribute to a generic source.

How to win back attribution

Audit your pages and your competitor's pages with AiVIS.biz to compare extraction readiness. Identify where their signals are stronger. Fix your Organization schema, add datePublished metadata, and ensure your content makes specific, attributable claims. The goal is to make the extraction path to your domain clearer than the path to your competitor.

Frequently Asked Questions

Can AiVIS.biz compare my extraction readiness against competitors?: Yes. Alignment tier and above includes competitor tracking, which compares your audit score and extraction signals against competitor domains on shared topics.
Does publishing first help with AI attribution?: Yes. datePublished metadata can establish temporal priority. While AI models weigh many factors, having a verifiable earlier publication date is a meaningful attribution signal.