AiVIS is an AI visibility audit platform that measures how AI search engines; ChatGPT, Perplexity AI, Google AI Overviews, and Claude; interpret, rank, and cite your website content. Every finding is tied to evidence scraped from your live page. Run a free deep audit in 30+ seconds.
AiVIS includes operator workflows for keyword prioritization, competitor benchmarking, citation testing, report sharing, and reverse-engineering AI answers. Teams use these pages to run monthly baselines, execute focused optimization sprints, and verify score gains.
Free Observer tier uses Meta Llama 3.3 70B Instruct for primary AI analysis with Google Gemma 3 27B as fallback — fast and completely free via OpenRouter. Alignment tier uses GPT-4o Mini; Signal and Score Fix tiers use a triple-check pipeline. Signal and Score Fix subscribers get three independent AI model passes auditing every page: GPT-4o Mini for primary analysis, Claude 3.5 Haiku for peer critique and score adjustment, and GPT-4o Mini for final validation. This eliminates single-model bias and produces the most accurate AI visibility scores possible.
AI search engines prioritize pages with 800-1500+ words of comprehensive, authoritative content. AiVIS measures word count, topical coverage, and whether your content provides genuine depth for AI citation.
The H1 heading tag is the primary signal AI systems use to classify your page topic. AiVIS verifies your page has exactly one H1, proper H2-H3 hierarchy, and keyword-relevant headings.
JSON-LD schema markup helps AI systems understand your content with precision. AiVIS detects Organization, FAQ, Article, Product, HowTo, and other schema types and recommends missing schemas.
Meta descriptions, Open Graph tags, and Twitter Card metadata help AI systems generate accurate summaries. AiVIS checks title length, description quality, and social sharing completeness.
HTTPS encryption, canonical tags, internal link structure, image optimization, and page load performance all affect how AI crawlers access and process your content.
Clear paragraph structure, FAQ-formatted content, bullet points, and explicit definitions improve how easily AI systems can extract facts and include your content in generated answers.
Each audit grades six dimensions: content depth, heading structure, structured data, metadata quality, technical SEO, and AI citability. Output includes an overall 0–100 visibility score, per category grades, and priority actions mapped to observed page evidence.
Plan allowances are live configured and may change over time. See the Pricing page for current monthly limits. Signal and Score Fix enable the three-model validation flow: AI1 primary analysis, AI2 peer critique, and AI3 final validation.
Pages are easier to cite when they combine explicit entity statements, complete JSON-LD markup, a single descriptive H1, and concise Q&A sections with concrete claims. AiVIS recommendations focus on those machine-readable signals first, then on secondary polish work.
Run a baseline audit, fix one high impact issue cluster, reaudit, and compare category deltas. Teams usually prioritize schema completeness, question/answer style section coverage, and canonical/meta accuracy before broader expansion.
If your page looks fine to humans but still gets skipped by answer engines, this is where you debug it. AiVIS shows what was extracted, what was missed, and what to fix next. Published by AiVIS (Intruvurt Labs, North Georgia, USA).
An AI visibility audit checks whether answer engines can confidently quote your page. Instead of only tracking rank positions, it tests structure, clarity, schema coverage, and citation readiness.
In plain terms: if your content is vague, thin, or hard to parse, AI systems will route around it. AiVIS shows those blind spots and gives your team a practical fix path.
All tiers run evidence-grounded analysis. Signal adds a three-pass review chain so major recommendations are critiqued and validated before they land in your report.
Each audit takes a minimum of 30 seconds to ensure thorough crawling, content extraction, schema parsing, and three rounds of AI evaluation. You can watch the real time progress of each step as it happens, from DNS resolution through report generation.
AiVIS grades your page on a practical A-F scale. Most first runs land in C or D; the point is to turn that into a clear roadmap, not a vanity score.
Depth still matters. Thin pages are harder to cite reliably. AiVIS checks whether your content gives enough substance for answer engines to quote without guessing.
The H1 heading tag is the single most important on-page signal for AI systems. It tells ChatGPT, Claude, and Google AI what your page is fundamentally about. AiVIS checks whether your page has exactly one H1 tag, whether H2 and H3 tags create a logical hierarchy per W3C web standards, and whether headings contain relevant keywords that match user search intent.
Schema.org JSON-LD markup is the language AI systems use to understand your content with precision. Without structured data, AI must guess what your page represents. AiVIS detects Organization, FAQ, Article, Product, HowTo, and other schema types defined in the schema.org vocabulary, validates their implementation per Google Search Central structured data guidelines, and recommends missing schemas that would dramatically improve your AI citability.
Meta descriptions, Open Graph tags, and Twitter Card metadata help AI systems generate accurate previews and summaries. AiVIS checks your title tag length, meta description quality, OG image dimensions, and whether social sharing metadata is complete and compelling.
Technical foundations affect how efficiently AI crawlers can access and process your content. AiVIS evaluates HTTPS encryption, canonical tags, internal and external link structure, image optimization with W3C image accessibility best practices, mobile responsiveness, and page load performance.
This category looks at extractability: can a model lift accurate facts quickly? Clear sections, direct definitions, and clean formatting usually improve citation odds.
Unlike generic SEO tools, AiVIS shows you exactly what it found on your page. The audit quotes your actual H1 tag, meta description, schema types, and key content excerpts. Each finding is labeled with a status: Pass (good), Needs Work (warning), Critical (serious issue), or Missing (not found). This transparency ensures every recommendation is grounded in your real content, not generic advice.
Most teams already have SEO dashboards. The gap is knowing why an answer engine skipped a page that looks "fine" to humans. AiVIS focuses on that gap and shows the exact fields models extracted or ignored.
That means fewer generic checklists and more practical edits: what to rewrite, what schema to add, and which structural issues are blocking citations right now.
Short version: less debate, faster fixes, clearer before/after proof.
AiVIS uses BRAG (Based Retrieval and Auditable Grading). Each scraped field is assigned a stable evidence ID (for example: ev_title, ev_h1, ev_schema). Recommendations are expected to reference those IDs, so teams can audit the why behind every suggestion. For model details, see OpenRouter docs.
Every audit is versioned and timestamped, creating a persistent trail that lets you track improvements over time. Each audit record includes the version number, exact timestamp, unique audit ID, and which AI models were used. This is essential for agencies managing multiple client sites, as it provides documented proof of optimization progress.
AiVIS offers four tiers designed for different use cases:
AiVIS is a U.S.-based service (EIN on file). We maintain 256-bit SSL encryption on all connections, enforce a strict no-data-resale policy, and collect only account-level data necessary to provide the service. Our platform uses no third-party analytics that track individual user behavior without consent.
An AI visibility audit measures whether AI systems like ChatGPT, Perplexity, Google AI, and Claude can clearly explain your business without guessing. AiVIS scores your website across six categories — content depth, heading structure, schema markup, meta tags, technical SEO, and AI readability — using a realistic A-F grading scale. Most sites score C or D.
Free tier uses Llama 3.3 70B Instruct for fast, zero-cost AI analysis with Gemma 3 27B fallback. Alignment tier uses GPT-4o Mini; Signal and Score Fix tiers use a triple-check pipeline. Signal and Score Fix subscribers get three independent AI model passes analyzing their website in sequence: GPT-4o Mini performs primary analysis, Claude 3.5 Haiku critiques the findings for accuracy, and GPT-4o Mini validates the final scores. Triple-check adds peer review and validation.
Each deep audit takes a minimum of 30 seconds. This ensures thorough DNS resolution, page crawling, content extraction, schema analysis, technical SEO evaluation, and three rounds of AI evaluation. Real-time progress is displayed for each step.
Yes. The Observer tier is free and includes a live monthly allowance. Paid tiers provide higher limits and advanced workflows. For current prices and limits, see the live Pricing page.
Traditional SEO tools measure keyword rankings and backlinks. AiVIS measures how AI systems actually interpret your content — whether ChatGPT can cite your expertise, whether Perplexity includes you in answers, and whether Google AI Overviews feature your information. This is a fundamentally different and increasingly important dimension of search visibility.
AI search engines synthesize information from hundreds of sources simultaneously. When ChatGPT or Claude evaluates whether to cite your page, they check if your content thoroughly answers the user's question. Pages with fewer than 800 words are rarely cited because they lack the depth AI systems need to generate confident, detailed answers. Aim for 800-1500+ words per key page.
The H1 heading tag is the primary title of your page. It tells AI systems what your page is fundamentally about. Every page should have exactly one H1 tag containing relevant keywords. Without a proper H1, AI crawlers struggle to classify your content and are far less likely to include it in generated answers.
Yes. Every audit creates a versioned, timestamped record in our database. Each audit trail includes the version number, exact timestamp, unique audit ID, AI models used, all category grades, content highlights, and full recommendations. This enables tracking optimization progress over time and provides documented proof for agency client reporting.
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