# AuraMetrics: Full Reference

> AuraMetrics is an AI visibility and GEO (Generative Engine Optimization) platform. It helps brands get found, cited, and recommended by AI systems like ChatGPT, Gemini, Claude, Perplexity, and Google AI Overviews. Plans from $35/month, billed monthly. Available in English and Spanish. This is the extended (full) version of the machine-readable bundle; the short index lives at https://aurametrics.io/llms.txt.

AuraMetrics is built on real patterns of how large language models choose, cite, and recommend sources. The platform centers on the GEO Score and its modules, grouped exactly as they appear in the product dashboard: GEO Pillars, AI Visibility, Content, AI Commerce, and Analytics. There are 23 modules in total.

Every analysis module runs against the most current criteria from Google and the major LLMs (GPT, Gemini, Claude, Perplexity), because what those models prioritize changes with each release. Reports include 0 to 100 scores, prioritized issues, and concrete recommendations, and most modules support competitor comparison on public URLs.

Pricing: Basic $35/month, Medium $119/month, Pro $259/month, and Enterprise (custom). There is a 5-day full-access trial for $5. Available in English and Spanish.

## Machine-readable access points

- [llms.txt](https://aurametrics.io/llms.txt): Short index of the most important pages and modules for AI agents.
- [llms-full.txt](https://aurametrics.io/llms-full.txt): This file. Full descriptions of pages, modules, and core concepts.
- [OKF knowledge bundle](https://aurametrics.io/okf/index.md): Open Knowledge Format (OKF) v0.1 bundle with canonical, machine-readable definitions of AuraMetrics concepts, metrics, integrations, and positioning.

## Homepage

- [AuraMetrics (EN)](https://aurametrics.io/en): English homepage. AI visibility platform for brands that want to be found and cited by ChatGPT, Gemini, Claude, Perplexity, and Google AI Overviews. Explains GEO, the GEO Score, and the module suite.
- [AuraMetrics (ES)](https://aurametrics.io/es): Pagina principal en espanol. Plataforma de visibilidad IA para marcas que quieren ser citadas por los motores de IA.

## Pricing

- [Pricing (EN)](https://aurametrics.io/en/pricing): Basic $35/month, Medium $119/month, Pro $259/month, Enterprise custom. 5-day full-access trial for $5. Pricing scales with the number of analyses and domains; competitor analyses are included with a monthly free allowance per domain.
- [Pricing (ES)](https://aurametrics.io/es/pricing): Basic $35/mes, Medium $119/mes, Pro $259/mes, Enterprise a medida. Trial de 5 dias con acceso completo por $5.

## Solution Pages

- [GEO, Generative Engine Optimization (EN)](https://aurametrics.io/en/geo): Complete guide to GEO, the discipline of appearing in ChatGPT, Gemini, Perplexity, and AI Overviews. Covers why generative engines select sources differently from classic search and how to optimize for citation rather than ranking.
- [AI SEO (EN)](https://aurametrics.io/en/ai-seo): How to rank in both traditional search and AI-generated answers, and where the two disciplines overlap and diverge.
- [AI Visibility (EN)](https://aurametrics.io/en/ai-visibility): How to measure and improve your brand's presence in AI search, including the signals models use to decide whether to mention or cite you.
- [AI Search (EN)](https://aurametrics.io/en/ai-search): What AI search is, how it works, and what it means for brands as users shift from blue links to direct AI answers.
- [eCommerce SEO and AI (EN)](https://aurametrics.io/en/ecommerce): Product feeds, Google Shopping, structured data, image SEO, and checkout optimization for AI engines and AI shopping agents.
- [SEO with AI (EN)](https://aurametrics.io/en/seo): How the AuraMetrics modules cover every aspect of modern SEO, from structured data to entity authority and trust.

## GEO Framework

- [GEO Framework (EN)](https://aurametrics.io/en/geo-framework): Open methodology for measuring brand visibility in AI search engines. Defines the GEO Score and its four pillars (Entity Authority, Technical Discoverability, Trust Signals, and Citability) and provides industry benchmarks. The GEO Score is a 0 to 100 composite that weights each pillar; pillars with no available data are excluded rather than penalized.
- [GEO Framework (ES)](https://aurametrics.io/es/geo-framework): Metodologia abierta para medir la visibilidad de marca en motores de busqueda con IA.

## Free Tools

- [Free AI Visibility Audit (EN)](https://aurametrics.io/en/free-audit): Free diagnostic that analyzes your site in about 30 seconds and shows how AI engines evaluate your brand for citation. Sends a full report by email.
- [Free AI Visibility Audit (ES)](https://aurametrics.io/es/free-audit): Herramienta gratuita que analiza tu sitio en unos 30 segundos y muestra como te evaluan los motores de IA.
- [AI Visibility Checker (EN)](https://aurametrics.io/en/tools/ai-visibility-checker): Check your brand's visibility across AI engines and see whether models recognize and recommend you.
- [Track AI Search Ranking (EN)](https://aurametrics.io/en/track-ai-search-ranking): Monitor your visibility in AI answers across ChatGPT, Gemini, and more over time.

## Industry Benchmarks

- [AI Visibility Benchmarks (EN)](https://aurametrics.io/en/benchmark): Industry-level AI visibility benchmarks across 15 industries and 5 regions. Shows how brands rank in ChatGPT, Gemini, Claude, and Perplexity answers so you can compare your position to your sector.
- [AI Visibility Benchmarks (ES)](https://aurametrics.io/es/benchmark): Benchmarks de visibilidad IA por industria y region.

## Comparisons

- [AuraMetrics vs Otterly AI (EN)](https://aurametrics.io/en/compare/otterly-ai-alternative): Feature comparison covering AI visibility tracking, competitive analysis, and prompt monitoring.
- [AuraMetrics vs Profound (EN)](https://aurametrics.io/en/compare/profound-ai-alternative): Feature comparison, AI visibility without enterprise overhead.
- [AI SEO Tools Comparison (EN)](https://aurametrics.io/en/compare/ai-seo-tools): What to evaluate before choosing an AI SEO tool.
- [AI Visibility Tools Comparison (EN)](https://aurametrics.io/en/compare/ai-visibility-tools): Comparison of AI visibility monitoring tools.
- [SEO Tools Comparison (EN)](https://aurametrics.io/en/compare/seo-tools): How AuraMetrics compares to traditional SEO tools like Ahrefs and Semrush, which optimize for classic ranking rather than AI citation.

## Glossary

- [AI Search and GEO Glossary (EN)](https://aurametrics.io/en/glossary): Definitions of AI visibility, AI citation, GEO, AEO, GEO Score, prompt tracking, llms.txt, and other key terms.
- [Glosario de AI Search y GEO (ES)](https://aurametrics.io/es/glossary): Definiciones clave para visibilidad en IA.

## Modules

AuraMetrics has 23 modules, organized exactly as they appear in the product dashboard: the GEO Score and its supporting groups (GEO Pillars, AI Visibility, Content, AI Commerce, and Analytics).

- [All Modules (EN)](https://aurametrics.io/en/modules): Complete catalog of AuraMetrics modules organized by group.
- [All Modules (ES)](https://aurametrics.io/es/modules): Catalogo completo de modulos de AuraMetrics organizados por grupo.

## GEO Score

- [GEO Score](https://aurametrics.io/en/geo-framework): The consolidated 0 to 100 measurement of how ready a brand is to be cited by AI engines. It aggregates the four GEO Pillar reports (Entity Authority, Technical Discoverability, Trust Signals, and Citability) into a single, trackable score and is the headline metric of the platform. Pillars with no data available are excluded from the calculation rather than counted as zero.

## GEO Pillars

The reports that make up the GEO Score: how AI identifies the brand, how discoverable it is, how trustworthy it appears, and how citable its content is.

- [AI Understanding](https://aurametrics.io/en/modules): Audits how clearly AI systems identify the brand as a distinct entity. It analyzes entity mentions, Schema.org (Organization, Person, Product, Brand), category co-occurrence, semantic isolation, and entity ambiguity, and checks connections to authoritative sources such as Wikipedia, Wikidata, LinkedIn, and Crunchbase, so AI associates the brand correctly in Knowledge Graphs and generated answers.
- [Schema Validator](https://aurametrics.io/en/modules): Validates Schema.org and JSON-LD structured data, detects missing schema types such as Organization, Product, FAQ, Article, and Breadcrumb, and generates ready-to-implement markup so AI engines can parse the brand's content correctly. Poorly implemented schema can confuse LLMs and make them discard a page.
- [Trust Auditor](https://aurametrics.io/en/modules): Evaluates the trust signals LLMs use to judge source reliability, including web mentions, sentiment, unlinked mentions, authority, data consistency, and brand-domain presence among cited sources, with a confidence gate that suppresses low-confidence findings. Returns a Trust Score and a prioritized improvement roadmap.
- [Content Readiness](https://aurametrics.io/en/modules): Analyzes each URL against the factors LLMs use to choose sources, including content depth, heading structure, freshness, citable data, E-E-A-T signals, structured data, and answerability. It also scores agent access by fetching and grading your llms.txt and llms-full.txt: whether they are well-formed (title and URLs), how many pages you expose to AI agents, and whether they reference your OKF bundle or an API. A deterministic Agent Read Window detector (no LLM) parses the raw server HTML, without running JavaScript, and computes how much of the first 5,000 to 6,000 characters (the read budget of AI agent fetchers) is real content versus navigational boilerplate, the character offset where the main content begins, and the number of links before it. Returns a citability score with specific, actionable recommendations, and supports competitor comparison on public URLs.

## AI Visibility

How visible the brand is across AI answers, engines, and competitors.

- [AI Visibility](https://aurametrics.io/en/ai-visibility): A consolidated panel showing how the brand appears across ChatGPT, Gemini, Perplexity, Claude, and Google AI Overviews, combining signals from multiple modules into one health view.
- [Prompt Tracking](https://aurametrics.io/en/track-ai-search-ranking): Tracks the brand's position and mention rate across a defined set of monitored prompts and AI engines over time, showing where the brand gains or loses ground.
- [AI Market Leaders](https://aurametrics.io/en/modules): Ranks which brands lead AI answers in a category, so a brand can see which competitors the AI engines currently favor and by how much.
- [Answer Monitor](https://aurametrics.io/en/modules): Tracks what ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews actually say about the brand, its products, and its competitors, including mention frequency and sentiment. It works like a clipping service for AI instead of traditional media.
- [AI Visibility Gaps](https://aurametrics.io/en/modules): Surfaces the sources and prompts where AI cites competitors but not the brand, turning each gap into a prioritized content and digital-PR opportunity.
- [Prompt Simulation](https://aurametrics.io/en/modules): Tests how LLMs respond to customer questions before content is published, so teams can validate messaging and find weaknesses before launch.
- [AI Perception](https://aurametrics.io/en/modules): A complete report on how leading LLMs currently describe the brand, including tone, framing, and competitive positioning, and detects hallucinations, inaccuracies, and critical omissions.
- [Citation Patterns](https://aurametrics.io/en/modules): Uses a machine-learning model to estimate the brand's AI citation probability, identify which signals correlate with citation, and simulate improvement scenarios against industry benchmarks.

## Content

Evaluate, improve, and create content for AI citation.

- [Content Optimization](https://aurametrics.io/en/modules): Cross-references data from every module to produce a prioritized, quantified roadmap, estimating how much each action would improve the GEO Score.
- [Content Creator](https://aurametrics.io/en/modules): Generates AI-optimized content grounded in real citation data and structured for clean LLM extraction and attribution.
- [FAQ Generator](https://aurametrics.io/en/modules): Generates the questions audiences ask AI about a category and formats answers with FAQ schema (validated FAQPage JSON-LD), optimized for AI citation and featured snippets.
- [llms.txt Generator](https://aurametrics.io/en/modules): Crawls your site (sitemap, robots.txt, and internal links, with SSRF protection), classifies each page as allowed, disallowed, or optional, and generates a ready-to-use llms.txt in the standard format, plus an optional llms-full.txt with extended per-page descriptions. Fully deterministic, with no AI calls, and grounded in the pages that actually exist on your site. Output is ready to copy or download.
- [OKF Generator](https://aurametrics.io/en/modules): Crawls your site and generates a complete Open Knowledge Format (OKF) v0.1 bundle as a downloadable ZIP: one markdown file per page with YAML frontmatter, classified into concept types (Product, Concept, Article, Documentation, API, Playbook), plus an index.md and log.md. Fully deterministic, with no AI calls, and grounded in real pages. Drop the /okf/ folder into your project's /public/ directory and reference it from your llms.txt.

## AI Commerce

Catalog, merchant feed, product images, and agentic-commerce readiness.

- [Search-Ready Titles](https://aurametrics.io/en/ecommerce): Rewrites product feed titles and descriptions based on real customer searches so products surface in Google Shopping and in AI shopping recommendations.
- [Image Fixer](https://aurametrics.io/en/ecommerce): Analyzes product images for AI agent readability and Google Shopping policy compliance, flags issues image by image (approved, at risk, likely rejected), and prioritizes fixes.
- [Agent Merchant](https://aurametrics.io/en/ecommerce): Audits whether the catalog is ready for AI shopping agents. It integrates Google Shopping, pulls product ratings and review sentiment, runs a competitor win and loss analysis per product attribute, applies a brand homograph guard, and verifies listing ownership with is_merchant matching. It also covers AI checkout readiness and a UCP / Web Payments manifest audit, and checks robots.txt access for AI agents.

## Analytics

GA4 traffic, revenue impact, and tracking quality.

- [AI Impact](https://aurametrics.io/en/modules): Measures the real referral traffic arriving from AI engines such as ChatGPT, Perplexity, and Gemini by combining GA4 and Google Search Console, quantifying the business impact of AI visibility.
- [Data Quality](https://aurametrics.io/en/modules): Audits GA4 tracking integrity, detecting misconfigured or duplicated events, missing tags, and broken tracking that corrupt reporting before they affect decisions.

## Core Concepts

- GEO (Generative Engine Optimization): The discipline of optimizing a brand and its content to be selected, cited, and recommended by generative AI engines (ChatGPT, Gemini, Claude, Perplexity, Google AI Overviews). Unlike classic SEO, which optimizes for ranking in a list of links, GEO optimizes for being chosen as a source inside an AI-generated answer.
- AEO (Answer Engine Optimization): A closely related discipline focused specifically on appearing as the direct answer in answer engines such as Google AI Overviews and Perplexity. Question-and-answer content with FAQPage schema is one of the highest-leverage AEO tactics.
- GEO Score: A 0 to 100 composite that estimates how likely AI engines are to cite a brand. It is computed as a weighted combination of four pillars; any pillar with no available data is excluded from the calculation rather than counted as zero, so the score reflects the signals actually present.
- The four pillars: Citability (is the content quotable, well-structured, answer-first, and citable), Entity Authority (is the brand a recognized entity connected to the Knowledge Graph and authoritative sources), Trust Signals (E-E-A-T, mentions, sentiment, data consistency, reputation), and Technical Discoverability (structured data and machine-readable markup that engines can parse).
- E-E-A-T: Experience, Expertise, Authoritativeness, and Trustworthiness. Originally Google's page-quality criteria, inherited by LLMs when they decide which sources are reliable enough to cite.
- llms.txt and agent access: A plain-text Markdown file at the root of a domain (/llms.txt) that gives AI agents a structured entry point to a site's most important pages and concepts. An optional /llms-full.txt provides the full expanded context in one fetch. AuraMetrics treats the presence and quality of these files (well-formed structure, number of pages exposed, references to a knowledge bundle or API) as an Agent Access signal within Content Readiness, because llms.txt is the first thing a well-behaved agent reads.
- Agent Read Window (fetch budget): AI agent fetchers such as GPTBot, ClaudeBot, PerplexityBot, and ChatGPT Deep Research fetch raw server HTML without running JavaScript and truncate their read at roughly 5,000 to 6,000 characters. The Agent Read Window is that fetch budget, and AuraMetrics measures deterministically how much of it a page spends on real content versus navigational boilerplate (global nav, headers, footers, link-dense blocks). On pages with heavy global navigation, most of the window is consumed by nav links and the real content is never read, so it is never cited. It is a detector inside Content Readiness and an emerging GEO topic that no major competitor exposes as a deterministic product metric.
- Knowledge Graph and entities: LLMs organize knowledge as entities (brands, people, products, places, concepts) rather than keywords. Being a verifiable entity connected to Google's Knowledge Graph, Wikidata, and other authoritative sources, with consistent NAP (Name, Address, Phone) data, is a primary driver of whether a model can identify and cite a brand.
- AI shopping agents: Autonomous agents that browse, compare, and increasingly purchase products without direct human intervention. They depend on machine-readable catalogs: structured product data, clear pricing and availability, crawlable pages, and access permitted in robots.txt.

## Blog

- [Blog (EN)](https://aurametrics.io/en/blog): Articles about generative SEO, analytics, and AI optimization.
- [Blog (ES)](https://aurametrics.io/es/blog): Articulos sobre SEO generativo, analytics y optimizacion para IA.

## Platform

- [Platform Overview (EN)](https://aurametrics.io/en/platform): How AuraMetrics works, including architecture, integrations, and capabilities.

## Contact and Social

- [Contact](https://aurametrics.io/en/contact): General inquiries and partnership requests.
- [Email](mailto:hello@aurametrics.io): hello@aurametrics.io
- [X / Twitter](https://x.com/aurametricsio): Updates, insights, and announcements.
- [LinkedIn](https://www.linkedin.com/company/aurametrics-io/): Company page and professional updates.
- [Product Hunt](https://www.producthunt.com/products/aurametrics): Product Hunt profile.

## Legal

- [Terms and Conditions (EN)](https://aurametrics.io/en/terms)
- [Privacy Policy (EN)](https://aurametrics.io/en/privacy)
- [DPA (EN)](https://aurametrics.io/en/dpa): Data Processing Agreement (GDPR).
- [Security (EN)](https://aurametrics.io/en/security): How AuraMetrics protects your data.
- [Subprocessors (EN)](https://aurametrics.io/en/subprocessors): List of subprocessors.