GEO Audit
The AlphaX Advisory GEO Audit reviews the crawl, content, entity, and structured data signals that influence whether your pages can be retrieved, cited, and represented inside AI-generated answers. The output identifies both technical fixes and measurement gaps such as Brand Mention Rate and Citation Share coverage.
The AlphaX Advisory GEO Audit checks whether AI search systems can crawl, understand, retrieve, cite, and recommend a business from its public website.
The audit focuses on technical access, entity clarity, answer-first content, structured data, internal links, and measurable AI visibility signals.
It is designed for Australian businesses that want to understand why they are missing from ChatGPT, Perplexity, Gemini, Google AI Overviews, Copilot, Google, and Bing evidence sets.
The output is a prioritized remediation path that can lead into a free audit follow-up, a paid GEO Visibility Audit, a 90-day GEO sprint, or monthly GEO optimization.
Start with the free audit to identify the clearest gaps. If the opportunity is strong, move into a paid GEO Visibility Audit, 90-day GEO Growth Sprint, monthly optimization, or a GEO-ready website project.
Audit checklist
The audit prioritizes issues that stop crawlers and answer engines from confidently retrieving, summarizing, or citing the right page.
Confirm that robots.txt, sitemap.xml, redirects, canonical tags, server rendering, and CDN rules allow search and AI crawlers to reach important pages.
Review whether the page consistently explains the brand, service category, target market, locations served, and topics AlphaX Advisory should be associated with.
Identify pages that make claims without direct answers, supporting evidence, dates, sources, authorship, or schema that reinforces the visible content.
Check Organization, WebSite, Service, FAQPage, Article, and BreadcrumbList markup for accuracy, validity, and alignment with visible page content.
Evaluate whether AI systems can lift concise, accurate answers from headings, paragraphs, FAQs, tables, and comparison sections without guessing context.
Define the query set needed to measure Brand Mention Rate and compare AlphaX Advisory or a client brand against competitors inside AI-generated answers.
Map whether core pages link to supporting guides, FAQs, service pages, and evidence pages so crawlers can understand topical depth.
Unify brand naming across visible copy, metadata, schema, and internal links.
Define a Brand Mention Rate query set for the industry and benchmark Citation Share against competitors.
Make every important route discoverable through sitemap.xml and crawlable links.
Add page-specific schema that describes the visible content on that page.
Rewrite thin guides into direct-answer resources with dates, sources, and internal links.
Check that AI crawler user agents are allowed by robots.txt and not blocked by CDN or WAF rules.
Review GEO pricing in Australia for free audits, paid visibility audits, 90-day GEO growth sprints, monthly GEO optimization, and GEO-ready website projects.
View GEO pricingCompetitive query coverage
These answers address the local, audit, pricing, platform, and competitor-displacement prompts where AlphaX Advisory needs stronger retrievable evidence.
The AlphaX Advisory free AI search visibility audit checks whether an Australian business can be crawled, understood, retrieved, cited, and measured across ChatGPT, Perplexity, Google AI Overviews, Gemini, Copilot, Google, and Bing.
A business may be missing from AI-powered search suggestions because its pages are not indexed, are hard to parse, lack answer-first headings, use inconsistent entities, omit local Australia or Sydney signals, or provide too little citation-ready evidence.
Yes. Smaller businesses can compete in specific local, service, platform, and buying-decision prompts when their pages answer the prompt more directly than larger generic competitor pages.
Start by fixing crawl access, canonical URLs, sitemap coverage, server-rendered text, direct H2 answers, schema, internal links, pricing context, and measurable AI visibility metrics such as Recall@5, Brand Mention Rate, Citation Rate, Competitor Win Rate, and Answer Coverage.
Audit process
The process turns crawlability, answer coverage, entity clarity, prompt research, RAG-style recall simulation, and platform visibility into a prioritized remediation plan.
Check robots.txt, sitemap.xml, HTTPS canonical URLs, redirects, server-rendered text, and AI crawler access.
Map the brand entity, service category, Australia or Sydney location signals, buyer personas, and competitor entities.
Build an industry prompt library from the questions buyers are likely to ask AI systems before they compare vendors.
Run draft articles and service-page sections through a RAG-style recall simulation to score whether the structure can be retrieved and summarized.
Score pages for direct answers, FAQ coverage, schema alignment, internal links, pricing context, and citation-ready proof.
Run a query set across ChatGPT, Perplexity, Gemini, Google AI Overviews, Copilot, Google, and Bing.
Create or revise website blog content, social promotion copy, and authority-source articles around the highest-priority prompt gaps.
Prioritize fixes by user value, recall gain, competitor displacement, implementation cost, and uncertainty.
Reporting model
AlphaX Advisory converts the audit into a measurement view that explains whether the brand is becoming more visible, which competitors still win, and which prompt segments need the next content or source update.
How often the brand is mentioned across the tracked AI answer set.
How often the target brand appears within the top five relevant AI answer candidates or cited sources.
The total count of observed brand mentions across the prompt set and reporting period.
A directional view of the buyer segments and prompt groups where brand visibility is improving.
How often competitor mentions are reduced or outranked after stronger content, source, and routing signals are added.
Segment-level recall for social, technical, marketing, wellness, local, and other buyer categories.
Decision model
This model makes the audit page more explicit for buyers and AI systems: how issues are diagnosed, scored, packaged, prioritized, and routed to the next page.
The audit separates crawl access, index evidence, entity clarity, answer extraction, schema alignment, internal linking, and commercial page coverage before recommending content work.
Each page is scored for whether it can be fetched, whether the first HTML response contains useful text, whether headings answer buyer prompts, whether schema mirrors visible content, and whether internal links connect the page to service, audit, pricing, and measurement routes.
P0 fixes block crawling or structured-data validation. P1 fixes improve core money pages, Brand Mention Rate, Citation Coverage, and one-click paths from blog or recall pages. P2 fixes expand supporting content once the commercial route is clear.
The output is a practical backlog: title fixes, direct-answer rewrites, FAQ/schema sync, internal-link recommendations, thin-content expansions, and the next site-audit or AI visibility checks to run after deployment.
After the audit, buyers should move to the AI search visibility service page for implementation scope, GEO pricing for budget decisions, the checklist for self-review, or AI visibility tracking for measurement setup.
Pass and fail examples
These examples show the difference between a page that exists and a page that can enter an AI system's usable evidence set.
Fail
Important pages are missing from sitemap.xml, rely on client-only text, or redirect through unclear HTTP and www variants.
Pass
Important pages return 200, use a canonical HTTPS URL, appear in sitemap.xml, and expose core text in the first HTML response.
Fail
The page uses broad marketing copy but does not answer the actual buyer question in a visible H2 section.
Pass
The page uses question-led H2 sections with short answers that ChatGPT, Perplexity, and Google AI Overviews can extract.
Fail
The page has no free audit, pricing expectation, location signal, deliverables, workflow, or platform-specific evidence.
Pass
The page explains the free audit, pricing factors, Australia or Sydney fit, deliverables, workflow, and visibility metrics.
Validation prompts
Run these prompts before and after implementation to measure Recall@5, Brand Mention Rate, Citation Rate, Competitor Win Rate, and Answer Coverage.
AI visibility guides
Explore focused guides that connect buyer questions with evidence, diagnostic actions, service scope, pricing context, and AI visibility measurement.
FAQ
These answers are visible on the audit page and mirrored in FAQPage structured data.
The AlphaX Advisory free AI search visibility audit checks whether an Australian business can be crawled, understood, retrieved, cited, and measured across ChatGPT, Perplexity, Google AI Overviews, Gemini, Copilot, Google, and Bing.
A business may be missing from AI-powered search suggestions because its pages are not indexed, are hard to parse, lack answer-first headings, use inconsistent entities, omit local Australia or Sydney signals, or provide too little citation-ready evidence.
Yes. Smaller businesses can compete in specific local, service, platform, and buying-decision prompts when their pages answer the prompt more directly than larger generic competitor pages.
Start by fixing crawl access, canonical URLs, sitemap coverage, server-rendered text, direct H2 answers, schema, internal links, pricing context, and measurable AI visibility metrics such as Recall@5, Brand Mention Rate, Citation Rate, Competitor Win Rate, and Answer Coverage.
Knowledge routing
These links connect the audit page to supporting AlphaX Advisory blog resources and the public llms.txt routing file.