What a technical GEO & AI visibility audit covers
The audit reviews how named AI crawlers (GPTBot, ChatGPT-User, PerplexityBot, Google-Extended, and ClaudeBot) access and render a Drupal site, bot by bot, rather than compressing everything into one generic "AI-readiness" score. It checks whether a llms.txt file exists at the domain root and evaluates whether adding one would help; this emerging standard gives AI models a clean, structured summary of a site's content instead of raw HTML. It also validates schema.org and structured data coverage, since machine-readable markup is one of the clearest signals both traditional search and AI answer engines use to disambiguate entities and content.
Content structure and semantic clarity come next: heading hierarchy, direct-answer paragraphs, and whether key facts sit in plain text rather than behind JavaScript rendering or interactive components. The engagement is scoped to public-facing pages only; intranets and authenticated content sit outside an AI-visibility audit by definition, since public AI crawlers never reach them.
Traditional crawlability, sitemap, and Core Web Vitals work stay out of scope too, and belong instead to Metadrop's technical SEO audit. So do editorial or keyword content strategy and accessibility or UX/UI review.
Why AI visibility matters now
Buyers increasingly start their research inside ChatGPT, Perplexity, and Google's AI Overviews rather than a classic search results page. A site those systems cannot read loses the first, and sometimes the only, touchpoint with a prospective buyer.
AI-driven bot traffic has grown sharply across the industry, and unmanaged crawler access can turn into a performance and cost problem in its own right, not only a visibility one.
An AI-visibility gap rarely announces itself the way a ranking drop does. There is no Search Console equivalent that flags "ChatGPT stopped citing you," so the gap can sit unnoticed for months. A site can rank well in classic Google search while remaining effectively invisible to AI answer engines, because the two systems parse content differently: a technical SEO audit and a GEO audit answer different questions.
What makes this a Drupal-native GEO audit
Every finding traces back to the specific Drupal module or configuration responsible for it, whether that's Metatag, Schema.org Blueprints, a robots.txt override, or the caching layer, rather than being left as a generic symptom description.
Where a llms.txt file would help, Metadrop can configure the llms.txt module directly in Drupal, using the platform's native Token system so the file stays current without manual upkeep.
Findings also account for Drupal's multilingual and multisite architecture: language negotiation, path-prefix setups, and domain-based multisite configurations, since AI crawlers encounter each language variant of a site independently.
Because the same team runs the audit and can carry out the remediation, findings map directly into Drupal implementation tickets, matching the posture behind Metadrop's technical SEO audit. This work also complements Metadrop's broader Drupal AI integration services, for organizations that want AI visibility and AI-powered site features addressed together.
Our audit process: 4 stages
Metadrop runs the engagement as four stages, from initial access to a prioritized fix list.
Scoping and access setup
Metadrop and the client first confirm which domains, languages, and content types are in scope, then arrange read access to server logs or a log-analysis tool to see current AI-bot request patterns. This stage also collects the baseline: existing robots.txt rules, any current llms.txt file, and existing structured-data coverage.Bot-by-bot access and rendering test
Access and rendering are tested **per named AI crawler** (GPTBot, ChatGPT-User, PerplexityBot, ClaudeBot, Google-Extended) against representative page templates. Structured data output is validated against current schema.org and Rich Results requirements, and content extractability is assessed: JavaScript dependency, heading structure, and whether paragraphs answer the question up front.Expert analysis and prioritization
Every finding is classified by impact tier (Critical, High, Medium, or Low) and mapped to its Drupal-level root cause, with fixing effort estimated per finding in story points or days so it slots directly into sprint planning. Findings are then grouped into thematic work packages, mirroring the methodology behind Metadrop's technical SEO audit.Findings report and remediation roadmap
The final deliverable opens with an executive summary covering overall AI-visibility posture, the top findings, and the available quick wins. A full findings report follows, with one entry per issue covering its root cause, fix instruction, and effort estimate, plus a roadmap that sequences the work by impact-to-effort ratio. Metadrop presents this roadmap in a session with technical and marketing stakeholders together.
They've Trusted Metadrop
From humanitarian NGOs to industrial manufacturers, organizations across 50+ countries have relied on Metadrop for Drupal audit and technical-governance engagements, including AI-readiness and search-visibility work.
Who needs a technical GEO & AI visibility audit
This audit fits a site when one or more of the following is true:
Traffic from AI referrers has dropped or never appeared
Analytics show no referral traffic from AI answer engines, despite reasonable organic search rankings.
A competitor is regularly cited
ChatGPT or Perplexity cite a competitor for queries your organization should be winning.
A major site relaunch or migration is planned
AI-readiness needs to be designed in from the start rather than retrofitted afterward.
No one has verified crawler access
It's unclear whether GPTBot, PerplexityBot, or Google-Extended can actually reach and parse the site's key pages.
Leadership is asking about AI or GEO strategy
Marketing needs a concrete technical baseline to answer from, not a guess.
A previous technical SEO or accessibility audit already ran
AI visibility is the remaining unaudited layer.
Metadrop as your AI visibility partner
Metadrop is a Drupal Silver Certified Partner with 15+ years of production Drupal experience across 50+ countries, holding ENS certification and a GDPR-ready, WCAG-compliant delivery discipline that carries into this audit's own governance and reporting.
Metadrop has published technical guidance on the llms.txt standard, covering its structure, content-selection criteria, and Drupal Token-system implementation, alongside analysis of AI-driven bot traffic's operational impact on site performance and how Drupal-level and CDN or reverse-proxy caching can mitigate it. The audit's findings combine automated bot-access testing with expert manual review of structured data and content structure, carried out by an independent expert partner engaged directly with the client's own team.
Frequently asked questions
What is a technical GEO & AI visibility audit?
A technical GEO & AI visibility audit is a systematic review of how AI crawlers, including GPTBot, ChatGPT-User, PerplexityBot, and Google-Extended, access, parse, and cite a website's content. It covers crawler access rules, whether a llms.txt file is present, structured data, and content or semantic clarity, and it stays separate from editorial or content SEO strategy.How is a GEO audit different from a technical SEO audit?
Technical SEO targets traditional search-engine crawling, indexing, and ranking signals, such as sitemaps, canonical tags, and Core Web Vitals. GEO, or AI visibility, targets how large language models and AI answer engines parse, summarize, and cite content instead. The two overlap on structured data and clean semantic HTML, but they answer different questions; see Metadrop's technical SEO audit for the crawlability and indexation layer.
What is llms.txt, and does Metadrop implement it?
llms.txt is a proposed standard: a markdown file at the site root that gives AI models a structured, low-noise summary of a site's key content. Metadrop evaluates whether a llms.txt file, and optionally a llms-full.txt file, would benefit a given site, and where it would, can implement it in Drupal using the llms.txt module's native Token-system integration.
How does ChatGPT decide which websites to cite?
Answer engines favor content that is structured, unambiguous, and easy to extract without heavy client-side rendering. Clean semantic HTML, schema.org markup, and a direct, answer-first narrative all improve how easily a page can be extracted. Citation behavior varies by model and changes over time, so the audit benchmarks current behavior rather than promising a fixed outcome.Why isn't my site showing up in AI Overviews or chatbot answers?
Common causes include blocked or rate-limited AI crawler user agents, JavaScript-rendered content that bots cannot execute, incomplete structured data, or the absence of a llms.txt file. The audit tests access bot by bot and maps each finding back to its Drupal-level root cause.Does allowing AI bots to crawl my site affect performance?
AI crawler traffic has grown across the industry and can add real server load when left unmanaged. The audit reviews rate-limiting, caching layers such as a CDN or reverse proxy, and Drupal-level configuration, with the goal of letting AI access and site performance coexist by design.How long does a technical GEO & AI visibility audit take?
The engagement is comparable in scope to Metadrop's technical SEO audit, and the scoping call sets an accurate timeline based on site size and the number of languages in scope. A single-site engagement typically runs a matter of weeks from access setup to the final report.