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's 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 GEO / AI-search-tooling space, these three signals tell us whether AI crawlers can even reach and trust resonatelabs.co. All three are derived mechanically from the Layer 1 site scan — they orient the rest of this document.
AI search is reshaping how mid-market B2B SaaS marketing teams discover and shortlist vendors — and Resonate Labs sells directly into that shift. The category is Generative Engine Optimization (GEO): AI search visibility audits plus done-for-you content execution that get mid-market B2B SaaS brands mentioned, won, and cited by AI answer engines. With 94% of B2B buyers now using LLMs during the buying process (6sense, November 2025), the brands that establish AI-answer visibility now compound an advantage that's hard to unwind — early citations make a domain more likely to be cited again. The opportunity for Resonate is to demonstrate that authority in its own category before the GEO market crowds.
This document validates the inputs that will drive your audit, not the results. Three things shape the query set we build: the competitive landscape (which vendors buyers weigh you against), the buyer personas (whose search intent we model), and the technical baseline (whether AI crawlers can access your content at all). Each section below asks you to confirm or correct what we've assembled — we're validating these together before the audit runs, and the corrections you make here are what keep the query architecture honest.
The validation call is a working session with real stakes. It resolves two kinds of decisions: (1) input validation — are the right competitors in the right tiers, are the personas the people who actually sign, are the feature strengths honest? — and (2) engineering triage — which technical items can your team start on before results come back? The Pre-Call Checklist near the end aggregates every open question into one printable page. The single highest-leverage decision is whether your buyers compare you against monitoring tools or against GEO agencies — that one answer re-weights a large share of the query set.
Three things to keep in mind as you review the competitive set, personas, features, and pain points below.
What this is This is the foundation for your GEO audit — the knowledge graph that determines which buyer queries we test across ChatGPT, Claude, Gemini, and Perplexity. It is not the audit itself, and it deliberately contains no content-gap analysis or content recommendations. Those require query-response data to prioritize properly and arrive in the full audit deliverable. Everything here is either an input to validate or a Layer 1 technical fix to hand to engineering.
What we need from you Tell us what's right, what's wrong, and what's missing. The purple boxes throughout this document are the high-value questions — each one names a specific entity and explains what changes in the audit if your answer differs from our assumption. Come to the validation call ready to answer them.
Confidence badges Every entity carries a confidence badge. High = directly observed (scraped from your site or a category listing). Medium = inferred from strong signals. Low = a reasonable hypothesis we most need you to confirm. Because GEO is a brand-new (2025–2026) category with no G2/Capterra/Gartner review base, every persona and pain point is synthesized rather than review-mined — so they warrant a close read.
The base facts that anchor every downstream input. Confirm these read the way you'd describe yourself to a buyer.
→ Validate Resonate straddles two buying conversations: self-serve AI-visibility tools (Profound, Peec AI, Otterly.ai) and GEO/content agencies (Omniscient Digital, Foundation, Single Grain). When a buyer evaluates you, are they more often comparing you against a monitoring tool or against an agency engagement? If it's tools, the head-to-head set weights toward "best AI visibility platform for B2B SaaS"; if it's agencies, toward "best GEO agency / done-for-you GEO service" — and a different competitor list surfaces in each answer. This is the single highest-leverage answer for query construction.
5 personas — 2 decision-makers, 2 evaluators, 1 influencer. Personas drive the query set: each searches differently, so each defines a distinct cluster of buyer intent we'll test.
Critical review area Personas are the input most worth scrutinizing. If a persona's role or authority is wrong, every query we build for them inherits the error. Read these as "is this the person who actually evaluates and signs?" — not "is this a plausible job title?"
Data sourcing note Because GEO has no established review corpus (no G2/Capterra/Gartner presence yet), every persona here is synthesized from category research and standard B2B SaaS buying-committee patterns (llm_inference), not review-mined. KG-sourced fields: role, department, seniority, influence level, veto power, technical level. Synthesized for this document: role description, primary buying jobs, and query focus areas. The validation call is where we confirm these against your real deal cycles.
→ Is the CMO the economic buyer who signs, or does the Founder/CEO hold final budget approval at this deal size? If the CEO signs, validation-stage queries should center "is GEO real or hype" skepticism rather than the CMO's channel-mix lens.
→ Does demand gen own the GEO budget line, or only influence it? If they own it, we add a cluster of pipeline- and ROI-justification queries to the evaluator set; if they only influence, those queries belong to the CMO instead.
→ Is the SEO & Content lead a champion who brings Resonate in, or a skeptic defending existing SEO spend? Champion vs. blocker flips whether we test "GEO vs. SEO" queries as opportunity framing or as objection-handling.
→ This is our lowest-confidence persona. Does product marketing actually sit in GEO buying decisions at your mid-market targets, or is this really a growth/demand-gen-owned purchase? If they're absent from real deals, we drop their competitive-positioning query cluster and reallocate it to the evaluators.
→ At what deal size does the Founder/CEO stop being the buyer? Your Done-with-You membership ($12K first month) likely pulls the CEO into the room; the $1,650 Visibility Audit may not. If the buyer changes by entry product, we should segment queries by price tier rather than treat it as one motion.
Missing personas? These roles sometimes appear in mid-market B2B SaaS GEO deals — do they show up in yours? RevOps / Marketing Ops Lead (if they own attribution tooling and would scrutinize how AI visibility connects to pipeline), Head of Content / Managing Editor (if the person who'd actually consume the done-for-you content is a distinct evaluator from the SEO lead), and a founder-led-growth operator at smaller targets (if your sub-mid-market deals collapse the whole committee into one person). Who else shows up in your deals?
5 primary + 4 secondary competitors. Tier assignments determine which vendors we put you head-to-head against in the audit.
Why tiers matter Primary competitors get direct head-to-head queries ("Resonate Labs vs. Profound," "best AI visibility platform for B2B SaaS"); secondary competitors appear in broader category-awareness queries. At roughly 6–8 queries per primary pairing, the five primary tiers drive on the order of 30–40 head-to-head queries. Two primary competitors — Omniscient Digital and Foundation — are medium-confidence on tier: both are agencies, and whether they belong as primary hinges on whether your buyers compare you to agencies or to tools (the company-profile question above). If agencies rarely appear in your real deals, moving them to secondary shifts roughly a dozen queries out of the head-to-head set.
→ Validate the set Three questions: (1) Who's missing? Any vendor that shows up in your deals — a tool, an agency, or an in-house build — that isn't here. (2) Tier accuracy: Do Omniscient Digital and Foundation (both medium-confidence) actually appear in your competitive deals, or are they category neighbors you rarely lose to? And is Scrunch AI (low confidence) real enough to keep, or noise? (3) Irrelevant? Any listed vendor your buyers never mention — telling us now keeps those queries out of the set.
11 buyer-level capabilities mapped. Features determine which capability queries the audit runs — and where strength ratings say you should compete vs. play defense.
Show me exactly where my brand appears, wins, and gets cited across ChatGPT, Claude, Gemini, and Perplexity — and where I'm completely invisible.
Test the real questions my buying committee actually asks AI when choosing a tool, not generic keyword prompts.
Tell me how often AI recommends my competitors instead of me and where they're beating me in answers.
Don't just show me the gaps — tell me the exact content to publish to start getting cited.
Actually write, build, and ship the content for me — my team doesn't have the bandwidth to execute.
Track whether my AI visibility is actually improving month over month after I publish content.
Tell me which of my pages and which third-party sources the AI engines are actually citing.
Give me a live dashboard I can log into any time to check my AI visibility scores and trends.
Prove that AI visibility actually drives demos and pipeline so I can justify the spend to my CFO.
I want a credible partner who explains how this works and advises me, not a black-box score.
Plug into our CMS and git so published content goes live in our stack without a bunch of manual handoffs.
Which strengths do we lean on? Five capabilities are rated Strong — the audit tests all 11, but competitive-differentiation queries will emphasize about 3. Which of these best represents where Resonate Labs wins deals?
• Multi-Engine AI Visibility Measurement
• Buyer-Intent Query Simulation
• Prioritized Action Plan & Content Recommendations
• Done-for-You Content Execution
• Strategic Guidance & Methodology Transparency
→ Validate the ratings (1) Are the weak ratings right? We've rated Self-Serve Real-Time Dashboard and Pipeline & Revenue Attribution as weak because you deliver reports and content, not a live SaaS, and don't offer closed-loop attribution — exactly where Profound is strong. Confirm: are these genuine vulnerability gaps to defend, or do you compete here more than we think? (2) Is Competitive Share-of-Voice really only moderate? Against dedicated tools' continuous dashboards we rated it moderate — but if buyer-intent simulation makes your SoV more credible than a tool's, it may deserve strong. (3) Merge candidates / gaps: Do Multi-Engine Measurement and Citation & Source Attribution overlap enough to merge, and is any capability buyers ask about missing?
10 pain points: 6 high, 4 medium severity. The buyer language here is how we'll actually phrase queries — these are the words your buyers type into AI.
→ Validate the frustrations (1) Severity: We rated "can't prove GEO ROI" and "no bandwidth to execute" as high — do those actually stall your deals, or is "invisible in AI answers" the one pain that opens every conversation? The highest-severity pains get tested first. (2) Buyer language: Does this phrasing match how your prospects actually talk — or do they say something sharper we should put in the queries verbatim? (3) Missing pains? Three we'd expect in mid-market SaaS GEO deals: "a competitor published a comparison page and now they own the answer," "I don't even know what my AI visibility baseline is," and "our brand is being described wrong by AI." Do any of these come up?
What our crawl of 31 pages found. These are technical fixes engineering can hand off now — not content recommendations, which the full audit will prioritize against query results.
For engineering to verify Good news first: there are no critical or high-severity blockers. robots.txt is present and explicitly allows GPTBot, ClaudeBot, PerplexityBot, and Google-Extended, so AI crawlers can reach the site. The work here is verification and reinforcement, not rescue. Engineering's two highest-value checks: (1) confirm content is server-rendered via a JS-disabled fetch — AI bots don't run JavaScript; and (2) confirm JSON-LD schema (especially FAQPage, given the site's heavy FAQ use) is present and valid. The one diagnostic content fix — thin copy on /brief/ and /for/ — is owned by Content.
What we found: Three high-traffic entry pages carry very little citable body text. The free-snapshot conversion page (/brief/, ~220 words, two H2s, no H3s) and the role hub (/for/, ~280 words) score below 0.4 on content depth, and the resources index (/resources/) is almost entirely navigational. These are among the first pages an AI crawler reaches from the homepage and primary nav.
Why it matters: AI answer engines cite self-contained passages that make a complete claim. Pages that are essentially a form or a list of links give the engines almost nothing to extract, so they consume crawl budget and internal-link equity without ever becoming citable themselves. On a site that otherwise has deep, citable content, these thin entry points are a structural weak spot rather than a content-strategy gap.
Recommended fix: Add 150–300 words of substantive, self-contained copy above or alongside the form/links on /brief/ and /for/ — e.g., what the snapshot measures and a concrete example finding on /brief/, and a one-paragraph framing per role on /for/. Keep the conversion element but give crawlers a citable passage.
What we found: The homepage and several product/landing pages (/sample-report/, /brief/, /for/) display no visible publish or "updated" date in the rendered content. The educational and comparison pages, by contrast, consistently show an "Updated June 2026" stamp. All pages carry a recent sitemap lastmod (2026-06-10 to 2026-06-14), so recency is detectable via the sitemap but not reinforced on-page.
Why it matters: A visible on-page date is a corroborating freshness signal for AI crawlers and a trust cue for human readers landing from an AI answer. Pages that rely on sitemap lastmod alone leave that signal implicit. This is a best-practice reinforcement, not a blocker — the sitemap already communicates recency.
Recommended fix: Add a visible "Updated {Month Year}" line to the homepage and the product/landing pages, matching the convention already used on the educational pages.
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: Every page returned full, substantive text through our rendered-markdown fetch — a strong indicator that content is server-rendered and reachable by crawlers that don't run JavaScript (the case study itself references shipping 38 server-rendered pages). But our method renders pages and can't definitively confirm whether any content is injected client-side only.
Recommended action: Spot-check key pages (homepage, /pricing/, a /compare/ page) with JavaScript disabled or via curl -A 'GPTBot', and confirm the primary body content is present in the raw HTML response.
What to check: Our analysis reads rendered page text and can't see JSON-LD structured-data blocks, so schema coverage could not be scored for any page (recorded as null across all 31 pages).
Recommended action: Run representative pages through a structured-data testing tool (Google Rich Results Test or Schema Markup Validator) and confirm Article / FAQPage / Organization schema is present and valid — particularly on the FAQ-heavy educational and comparison pages, where FAQPage markup is especially high-value.
What to check: Meta descriptions and Open Graph/social tags live in the raw HTML head and aren't exposed in rendered markdown, so they couldn't be assessed for any page.
Recommended action: Verify meta descriptions and OG tags via a social preview tool or view-source on a sample of pages, and confirm each commercial page has a unique, descriptive meta description and OG image.
Why now The GEO window is open and narrowing:
• AI search adoption is accelerating quarter over quarter — 87% of B2B software buyers say AI chatbots are changing how they research vendors, and half now start research in a chatbot rather than Google (G2, October 2025).
• Early citations compound: domains AI engines learn to trust now get surfaced more often as that behavior reinforces itself.
• Competitors who establish AI-answer visibility first create a structural disadvantage for late movers.
• GEO is still early-innings — and as a GEO firm, Resonate competing for visibility in its own category now means competing against inaction, not entrenched strategies. Gartner predicts 90% of B2B buying will be AI-agent-intermediated by 2028 (Gartner, October 2025).
Once validated, the full audit will measure citation visibility across the buyer queries that matter in the GEO space — from category questions like "best AI visibility platform for B2B SaaS" to head-to-head prompts like "Resonate Labs vs. Profound" and execution-intent searches like "done-for-you GEO content service." You'll see exactly which queries return answers that name your competitors but not Resonate Labs — and what it would take to appear in them. Fixing the Layer 1 items now (server-render verification, schema, and the thin entry pages) strengthens your baseline before we even start measuring.
45–60 minutes to walk through this document, confirm the inputs, and resolve the open questions in the checklist below.
We build the validated buyer queries and run them across the selected AI platforms — ChatGPT, Claude, Gemini, and Perplexity.
Visibility analysis, competitive positioning, and a prioritized three-layer action plan — the content work, sequenced by what actually costs you citations.
Engineering can start now Three Layer 1 items don't depend on the rest of the audit and will improve your baseline before we measure it: (1) confirm server-side rendering with a JS-disabled / curl -A 'GPTBot' fetch on the homepage, /pricing/, and a /compare/ page; (2) validate JSON-LD schema (Article / FAQPage / Organization) on the FAQ-heavy pages; and (3) thicken the thin entry pages (/brief/, /for/) with a citable passage. robots.txt already confirms AI crawlers are allowed, so no access fix is needed — but a quick re-check after any deploy keeps it that way.
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.