Varazm AEO Analyzer scores any page against 36 answer-engine readiness factors and models how 18 AI search environments would read it — a clear score, a per-environment breakdown, and specific fixes.
18 AI engines · 36 ranking factors · actionable recommendations
Varazm AEO
Pricing
Simple monthly pricing
Two clear plans — a single one-off scan or a monthly Premium with up to 25 scans/day, full reports, PDF export, saved websites and priority AI scoring.
Pay-as-you-go
Single Scan
One complete AEO audit — perfect for a quick check without any subscription.
AEO stands for Answer Engine Optimization: the practice of making a website easy for AI assistants like ChatGPT, Claude, Gemini, Perplexity and Copilot to read, understand and cite when they generate answers.
Get cited by AI
Structured content, clean metadata and clear entity signals make AI models much more likely to name your brand in their answers.
Score on 36 factors
We measure metadata, structured data, FAQs, entities, freshness, crawlability, performance and more — technical and content signals that influence how AI systems crawl, understand and cite a page.
Actionable fixes
Every low score comes with a specific, prioritized recommendation you can hand to a developer or apply yourself.
Why it matters now
Buyers increasingly ask an AI assistant instead of scrolling Google results. If your site isn't AEO-ready, AI engines describe your competitors — not you. AEO tunes structure, metadata, entities and content so AI models can quote you accurately and confidently.
How it works
How the scan works
1
Enter a URL
Paste any public URL — your homepage, a landing page, or a blog post.
2
Deep AEO scan
We parse HTML, JSON-LD, robots, sitemap and content signals against 36 ranking factors.
3
Per-engine scoring
AI models estimate how discoverable and citable your page is for each of the 18 answer engines we track — including ChatGPT, Claude, Gemini, Perplexity and Copilot.
4
Actionable fixes
Priority recommendations by severity — structured data, content, entities, freshness and more.
AI Engines
Readiness modelling across 18 AI search environments
Each profile re-weights the same audit for how that environment retrieves and cites pages. Readiness is an estimate. Live monitoring, on a subscription, measures what five answer engines actually say about you.
ChatGPT (GPT-5)
Live monitored
Claude 4
Live monitored
Gemini (PaLM 3)
Live monitored
Perplexity AI
Live monitored
Copilot (GPT-5 powered)
Live monitored
xAI Grok-3
Readiness only
Meta AI (Llama 4)
Readiness only
DeepSeek-V3
Readiness only
Mistral (Codestral/Le Chat)
Readiness only
Cohere Command R+
Readiness only
You.com
Readiness only
Arc Search
Readiness only
Phind
Readiness only
Aleph Alpha Luminous
Readiness only
Kore.ai
Readiness only
Replicate AI
Readiness only
Kagi Search
Readiness only
YouPro
Readiness only
Ranking Factors
36 ranking factors, one clear score
Each scan grades the technical, structural and content signals that influence crawlability, entity understanding and citation readiness across AI search and answer systems.
Structured Data
·01
Optimization of data formats (e.g., Schema.org JSON-LD) to help search engines and AI models understand content context.
Content Clarity
·02
Ensuring content is clear, concise, and directly answers user queries, avoiding ambiguity.
Semantic HTML
·03
Using appropriate HTML5 elements to convey meaning and structure to machines and AI.
Metadata
·04
Optimizing meta titles, descriptions, and other tags for AI summarization and presentation.
FAQ & Q&A
·05
Structuring content as Questions & Answers to directly feed into AI answer engines.
Entity Signals
·06
Clearly defining and linking entities (people, places, things, concepts) within content to aid AI understanding.
Freshness
·07
Ensuring content is up-to-date and signals its recency to AI models for timely answers.
Citations & Sources
·08
Providing clear, verifiable references for factual claims, crucial for AI answer generation.
Crawlability
·09
Making content easily discoverable and accessible for AI crawlers and indexing processes.
Performance
·10
Optimizing page load speed and responsiveness to ensure efficient AI processing and user experience.
Accessibility
·11
Designing content to be usable by everyone, including AI models that process diverse inputs.
Brand Authority & Trust
·12
Building trust and recognition for the brand as a credible source of information.
User Trust Signals
·13
Incorporating elements that reassure users and AI about the reliability and safety of content.
Multilingual Signals
·14
Strategies for presenting content in multiple languages and signaling language variations to AI.
Readability
·15
Optimizing content for easy comprehension by human readers and AI models.
Alt Text Coverage
·16
Ensuring descriptive alt text for all images to provide context for AI and accessibility.
Heading Hierarchy
·17
Using correct heading structures (H1, H2, H3, etc.) to outline content for AI parsing.
Internal Linking
·18
Strategically linking related content within a site to enhance AI's understanding of topics and relationships.
Direct Answer Formatting
·19
Formatting content to be easily extractable as concise, direct answers by AI engines.
Factual Accuracy
·20
Ensuring all factual statements are correct and verifiable to prevent misinformation by AI.
Brand Consistency Across Engines
·21
Maintaining a consistent brand identity, messaging, and tone across all platforms and AI interactions.
E-E-A-T Signals
·22
Demonstrating Expertise, Experience, Authoritativeness, and Trustworthiness to AI models.
Geographic/Local Signals
·23
Providing clear location-based signals for local queries and geographically relevant AI answers.
Machine Readability
·24
Optimizing content structure and language for efficient processing by AI algorithms.
Brand Voice Consistency
·25
Ensuring the unique tone and style of a brand are consistently applied in all content, including AI-generated summaries.
Source & Citation Reliability
·26
Assessing and enhancing the trustworthiness and credibility of information sources for AI interpretation.
Multimedia Optimization
·27
Optimizing images, videos, and audio for faster loading and better AI comprehension (e.g., compression, formats).
Multimodal Content Optimization
·28
Strategies for combining different content types (text, image, video) to create rich, AI-understandable experiences.
Information Architecture
·29
Designing the organization, labeling, and navigation of websites to facilitate AI information retrieval.
Content Originality
·30
Emphasizing unique, non-plagiarized content creation to stand out to AI models.
User Intent Alignment
·31
Crafting content that directly addresses the underlying intent behind user queries, as interpreted by AI.
Visual Content Optimization
·32
Optimizing images, infographics, and videos for AI-driven visual analysis and understanding.
Product Structured Data
·33
Implementing Schema.org markup for product details, reviews, and availability to enhance e-commerce visibility in AI answers.
HowTo Schema
·34
Using HowTo structured data to provide step-by-step instructions that AI can easily extract and present.
Scientific Accuracy
·35
Ensuring scientific claims are peer-reviewed, data-driven, and supported by robust research for high-trust AI contexts.
Data Transparency
·36
Openly disclosing data sources, methodologies, and potential biases to build AI trust and verifiability.
Platform capabilities
Beyond a scan — the full AEO command center
Every plan builds on the one‑shot AEO scan with continuous monitoring, competitor share‑of‑voice, site‑wide audits with AI patches, and a public API — so your brand keeps winning in ChatGPT, Gemini, Perplexity, Claude and Copilot.
AI Prompt Monitoring & Competitor Share‑of‑Voice
Add the prompts your customers actually ask AI engines. We run them on schedule across 5 engines, record whether your brand is mentioned, its rank position, which sources are cited, and how your Share‑of‑Voice compares to competitors — with 7 / 30 / 90 day windows.
Every AI answer is built from a handful of sources. Our bipartite Citation Graph shows exactly which domains each engine cites, how often, and where your own domains appear — so you know which PR, backlinks and content assets are actually working for AI visibility.
Bipartite graph: engines ↔ cited domains
Edge thickness = citation frequency
Your domains highlighted
Export to CSV for outreach lists
Site‑Wide AEO Audit + AI Fix Patches
Point us at your root URL. We crawl your sitemap, analyze every page for JSON‑LD (Organization, FAQ, Breadcrumb), meta, headings, images, canonicals and word count, score each page 0–100, and highlight entity consistency issues (name/logo/sameAs). Then Gemini generates a copy‑pasteable fix patch for the worst offenders.
Up to 50 pages per audit (200 on Scale)
Cross‑page entity consistency detector
AI patch = <title>, meta, H1, JSON‑LD, missing content
Bilingual patches — apply as EN or FA
Public API, Webhooks & dynamic llms.txt
Everything you see in the UI is available over a Bearer‑token REST API. Subscribe to HMAC‑signed webhooks for audit.completed, audit.failed and prompt_run.completed events. Publish a live llms.txt built from your brand + latest audit so AI crawlers get an authoritative brand snapshot.
Answer Engine Optimization (AEO) is the practice of preparing a website so AI answer engines can read, understand, quote and cite it. It combines technical access, semantic clarity and directly answerable content.
How is AEO different from SEO?
SEO optimises for a position in a list of results; AEO optimises for inclusion inside a generated answer. Most technical SEO work still helps, but AEO adds entity clarity, factual attribution and AI crawler permissions.
What is AI Visibility?
AI Visibility is how often your brand actually appears in answers produced by AI engines for a tracked set of prompts. It is measured from stored responses, not estimated from your page.
How does Varazm calculate readiness?
Readiness is computed from the page itself: 25 weighted factors grouped into technical, structural, semantic and content categories. The same page always produces the same score until the page changes.
Why do AI responses change between runs?
Generative models sample from a probability distribution, so the same prompt can produce different brands and sources each time. This is why a single run is never treated as a result — repeated runs are.
Does Varazm use estimated or live data?
Both, clearly separated. Readiness scores are estimated from your pages; visibility, citations, share of voice and sentiment come from live answers stored at run time. The interface never presents one as the other.
