Before we run the audit, we need to make sure we're asking the right questions about the right competitors to the right buyers. This document presents what we've learned about Resonate Labs' market — your job is to tell us what we got right, what we got wrong, and what we missed.
Before we measure citation visibility in the tech-enabled GEO services space, these three signals tell us whether AI crawlers can access and trust Resonate Labs' content. Two of three are flagged.
AI search is reshaping how B2B marketing leaders discover and shortlist tech-enabled Generative Engine Optimization services firms. Buyers are forming vendor shortlists inside ChatGPT, Perplexity, Gemini, and Claude before engaging with sales — and for a company whose entire value proposition is AI visibility, your own discoverability across these platforms is both a business imperative and a proof point. Companies that establish citation visibility now build a compounding advantage as AI platforms learn to trust cited domains.
This document presents two bodies of work for your validation. The competitive landscape identifies which vendors your buyers compare against in AI platform queries — tier assignments determine which head-to-head matchups the audit tests. The buyer personas map the roles who evaluate and sign GEO agency contracts, each generating distinct search intent across the buying journey. And the Layer 1 technical analysis reveals whether AI platforms can actually access your content — the critical finding here is that the Cloudflare managed robots.txt blocks GPTBot and ClaudeBot from indexing any page on the site.
Two types of decisions need to happen before the audit runs. First, at the validation call: confirm or correct the knowledge graph inputs — particularly whether the newly added Executive Buyer (CEO/CMO) persona accurately reflects how C-suite involvement works in your deals, and whether the two medium-confidence primary competitor tier assignments for Genevate and GenOptima hold, since these directly shape which queries test head-to-head differentiation versus category awareness. Second, for engineering immediately: unblock GPTBot and ClaudeBot in the Cloudflare dashboard and verify schema markup status on all three pages. These technical fixes don't require the call and will improve the baseline before we measure it.
Three things to know before you read further.
What this is This document presents the research foundation for Resonate Labs' GEO visibility audit. Every section feeds a downstream step: personas drive the buyer query set, competitors determine head-to-head matchups, features shape capability queries, and pain points provide the buyer language queries will be phrased in. The technical findings tell us whether AI platforms can reach your content at all.
What we need from you Look for the purple boxes throughout this document. Each one asks a specific question about something we need you to confirm or correct. Your answers directly change what the audit measures. A wrong persona means wasted queries. A wrong competitor tier means testing matchups that don't exist. Come to the validation call with answers to the purple questions.
Confidence badges Every data point includes a confidence badge: High means sourced from public data or direct evidence. Medium means inferred from category patterns or partial evidence — these are the ones most likely to need correction. Low means best-guess based on limited signal.
The baseline identity that anchors every query in the audit.
Validate The name variant "Resonate" may cause entity collisions with Resonate (the consumer data platform) in AI platform responses. Does Cited generate its own buyer search intent (e.g., "GEO optimization book," "AI visibility guide"), or is it purely a credibility asset for the audit service? If it drives its own discovery queries, we add a dedicated query cluster for the book.
5 personas: 3 decision-makers, 0 evaluators, 2 influencers. Each generates distinct search queries across the GEO agency buying journey.
Critical review area Personas drive the buyer query set — every role maps to a cluster of queries reflecting how that person searches during evaluation. A missing persona means an entire search intent pattern goes untested. A wrong persona means wasted queries that don't match real buying behavior.
Data sourcing Role, department, seniority, influence level, and veto power are sourced from the knowledge graph (provenance noted per card). Buying jobs and query focus areas are synthesized from the persona's role context and the GEO agency buying cycle — these are our best inference of how each role searches, not direct observations.
→ With the Executive Buyer (CEO/CMO) now in the persona set as a separate decision-maker, does the VP Marketing still hold independent budget authority, or does Sarah primarily build the business case that the CEO/CMO approves? If Sarah is a recommender rather than a signer, we reclassify her as evaluator and shift her query cluster from budget-justification to vendor-vetting queries.
→ Does the Director of Content Strategy typically surface GEO as an initiative and evaluate agencies, or does Marcus execute on a decision already made by the VP Marketing? If his role is execution rather than evaluation, we shift his query cluster to implementation-stage queries like "GEO content workflow" and "AI content optimization process."
→ Is demand gen a separate buying center from the VP Marketing for GEO services, or does Aisha's pipeline authority overlap with Sarah Chen's budget authority? If demand gen doesn't hold independent veto power in actual deals, we reclassify Aisha as an influencer and remove approximately 20% of demand-stage pipeline queries from the buyer set.
→ Does the Head of Digital Marketing typically discover and champion GEO agencies bottom-up, or is this initiative top-down from the VP Marketing or CEO? If Jordan isn't the internal champion who surfaces the category, we deprioritize awareness-stage technical queries and weight evaluation-stage queries instead.
→ Does the CEO/CMO actively research GEO agencies in AI platforms, or do they only review a shortlist prepared by the VP Marketing? If the executive buyer doesn't search independently, we remove their discovery-stage queries and add executive-format validation queries (board-ready ROI, competitive threat briefings) that the VP Marketing would share upward.
Missing personas? These roles sometimes appear in tech-enabled GEO services deals — do they show up in yours? Head of SEO / Organic Growth (if SEO is managed separately from digital marketing and the transition to GEO creates a turf concern). RevOps / Marketing Ops Lead (if attribution and measurement infrastructure decisions require a separate stakeholder). CMO at a portfolio company (if PE-backed brands are a distinct buyer segment with different approval dynamics). Who else shows up in your deals?
5 primary + 4 secondary competitors identified. Tier assignments determine which head-to-head matchups the audit tests.
Why tiers matter Primary competitors generate head-to-head comparison queries in the audit — queries like "Resonate Labs vs Omniscient Digital" or "best GEO agency for B2B." Getting these tiers right determines which approximately 30–40 queries test direct competitive differentiation versus category awareness. We're less certain about Genevate's and GenOptima's primary tier — both are medium-confidence newer entrants. If they rarely appear in actual deals, moving them to secondary would shift approximately 12–16 queries from head-to-head to category-level.
Validate Are there GEO agencies or AI visibility vendors appearing in your actual deals that aren't listed here? Do Genevate and GenOptima (both medium confidence) actually show up in competitive evaluations, or are they category peers you rarely encounter head-to-head? Should any secondary competitor — particularly HubSpot's AEO tools — be promoted to primary if buyers frequently compare their built-in features against hiring a dedicated GEO agency?
10 buyer-level capabilities mapped. Each feature generates capability queries in the audit — strength ratings determine whether those queries play offense or defense.
Run a comprehensive audit showing exactly where our brand appears — and doesn't appear — across ChatGPT, Perplexity, Gemini, and Claude for the queries our buyers actually ask
Show me which competitors are getting recommended by AI instead of us, how often they win, and what content is earning them those citations
Don't just hand me a report — build and deploy the content that will actually get us cited by AI platforms
We need visibility across all the AI platforms buyers use — ChatGPT, Perplexity, Gemini, Claude — not just one
Map the actual questions our different buyer personas are asking AI platforms across the entire buying journey — from problem identification to vendor evaluation
Category understanding is table stakes — we need a partner with an established methodology who has actually delivered measurable GEO results for clients in our category, not just published articles about it
Audit whether our website is technically set up for AI crawlers to read, index, and cite our content correctly
Track how our AI visibility changes over time so we can see if the content we're deploying is actually moving the needle
Prove that the AI visibility work is actually driving pipeline and revenue — tie citations back to traffic, leads, and deals
Give me a dashboard where I can log in anytime and see our AI visibility metrics, track progress, and share results with my leadership team
Strong feature prioritization The audit tests all 10 capabilities, but competitive differentiation queries will emphasize 3. Which of these best represents where Resonate Labs wins deals?
Validate Are the strength ratings accurate relative to Omniscient Digital, Graphite, and First Page Sage? Self-Service Reporting is rated "absent" and ROI Attribution "weak" — are these conscious positioning choices (consultancy model, not SaaS), or are they gaps you're actively closing? Are there buyer-level capabilities missing from this list — for example, "AI-native PR and media placement" or "vertical industry specialization"? Should any two features be merged because buyers don't distinguish them?
9 pain points: 7 high, 2 medium severity. Buyer language from these pain points drives how queries will be phrased in the audit.
Validate Are the severity ratings accurate? "Content Not Cited by AI" is now rated high — does this match the urgency you hear from buyers, or is it more of a medium-severity friction point? Is the buyer language authentic to how your prospects actually describe these frustrations, or does it read too polished? Missing pain points to consider: "Board/investor pressure on AI strategy" (if executive-level buyers feel pressure to have an AI visibility plan). "Internal team skill gap" (if buyers worry their marketing team can't execute GEO even with an agency). What's missing?
Layer 1 technical findings from the site analysis. These are engineering actions — not content recommendations.
Engineering: Start immediately The robots.txt finding is a critical blocker — GPTBot and ClaudeBot are blocked site-wide via Cloudflare's managed robots.txt, meaning OpenAI and Anthropic crawlers cannot index any page on resonatelabs.co. Engineering should allow GPTBot and ClaudeBot in Cloudflare's bot management settings before the validation call. The site footprint finding (3 pages total) is a structural observation — content expansion will be prioritized in the full audit based on query response data.
What we found: The robots.txt file (managed by Cloudflare) blocks four key AI crawlers: GPTBot (OpenAI/ChatGPT training), ClaudeBot (Anthropic/Claude), Google-Extended (Google AI training), and Bytespider (ByteDance AI). These directives prevent these crawlers from indexing any page on resonatelabs.co. ChatGPT-User (ChatGPT browse mode), PerplexityBot, and Googlebot are not mentioned and default to allowed. The robots.txt also includes a Content-Signal directive setting ai-train=no for all user agents.
Why it matters: GPTBot and ClaudeBot crawling is a prerequisite for content to enter the training data and retrieval pipelines of ChatGPT and Claude respectively. Blocking GPTBot means OpenAI cannot index site content for grounding or RAG-based responses. Blocking ClaudeBot prevents Anthropic from indexing the site. For a company whose entire value proposition is AI visibility, blocking the crawlers of the platforms you help clients get cited on creates a credibility gap and limits the company's own discoverability.
Recommended fix: Review the Cloudflare Managed robots.txt settings. Allow GPTBot and ClaudeBot to crawl the site (these are the content retrieval crawlers, not just training crawlers). Consider keeping Google-Extended and Bytespider blocked if the concern is training data usage. The Content-Signal ai-train=no directive already signals training opt-out without blocking retrieval crawling. Cloudflare dashboard → Security → Bots → Configure Managed robots.txt to selectively allow GPTBot and ClaudeBot.
What we found: The entire site consists of 3 pages: the homepage (resonatelabs.co), a brief request page (/brief/), and a visibility review page (/visibility/). The sitemap.xml contains only these 3 URLs. No blog, no case studies, no feature pages, no comparison pages, no documentation, no resource pages, no about page were found. The homepage navigation uses only anchor links to sections within the same page.
Why it matters: AI platforms construct responses by synthesizing content from multiple authoritative pages. A 3-page site provides minimal surface area for AI crawlers to index and cite. Each missing page type represents a class of buyer queries where Resonate Labs cannot be cited: no blog means no thought leadership for educational queries, no comparison pages means no presence in "vs" or "alternatives" queries, no case studies means no evidence for validation-stage queries.
Recommended fix: This is a structural observation rather than a technical fix. Content expansion priorities will be determined by query response data in the full audit. The audit will reveal which specific page types and topics would have the highest citation impact based on actual buyer query patterns.
The following items could not be assessed through our analysis method (rendered markdown). We recommend your engineering team verify these manually before the validation call.
What to check: Our analysis method cannot detect JSON-LD schema markup, meta descriptions, or OG tags. These signals are embedded in HTML head sections and are not visible in rendered output. Verify whether the site implements Organization, WebPage, Product, or other schema types.
Recommended action: Verify schema markup using Google's Rich Results Test or Schema.org Validator. At minimum, implement Organization schema on the homepage, WebPage schema on all pages, and verify meta descriptions and OG tags are present on all 3 pages.
What to check: All 3 pages returned substantive text content, suggesting server-side rendering is functional. However, confirm whether any content relies on client-side JavaScript rendering that might be invisible to AI crawlers with limited JavaScript execution.
Recommended action: Load the site with JavaScript disabled in browser DevTools (Settings → Debugger → Disable JavaScript). If all content renders without JS, no action needed. If content disappears, implement server-side rendering for affected sections.
Partial sample The analysis covers the complete site (3 pages), but this is an unusually small footprint. Content depth (0.40) and passage extractability (0.52) are below the healthy threshold, likely because the site concentrates content on a single homepage rather than distributing it across focused pages. Schema coverage could not be assessed from rendered output — engineering should verify manually.
Why now
The full audit will measure citation visibility across buyer queries in the GEO services space, including queries like "best GEO agency for B2B SaaS," "AI visibility audit services vs traditional SEO," and "how to get cited by ChatGPT." You'll see exactly which queries return results that include competitors like Omniscient Digital and Graphite but not Resonate Labs — and what it would take to appear in them. Unblocking GPTBot and ClaudeBot now means the audit measures your actual visibility potential, not an artificially suppressed baseline.
45–60 minutes walking through this document. Confirm personas, competitor tiers, feature strengths, and pain point severity. Your corrections directly shape the buyer query set.
Buyer queries derived from validated personas, features, and pain points are executed across ChatGPT, Perplexity, Gemini, and Claude. Every query maps to a real buyer intent.
Complete visibility analysis, competitive positioning across all platforms, content gap prioritization based on actual citation data, and a three-layer action plan.
Start now — don't wait for the call These technical fixes don't depend on the rest of the audit and will improve your baseline visibility before we even measure it:
Two jobs before we meet. The questions on the left require your judgment — no one knows your business better than you. The engineering tasks on the right don't require the call at all.