Security buyers now ask ChatGPT, Claude, and Perplexity for the shortlist — "best EDR for healthcare," "SOC 2-ready alternatives to [vendor]," "is [vendor] FedRAMP authorized" — before a single form loads. But a model won't confidently name a security vendor whose claims it can't substantiate, and it will quietly drop one it has filed in the wrong threat or compliance category. We get you recommended for the commercial security prompts that precede a deal by fixing the three things models gate on in this category — defensible evidenced claims, correct category framing, and citations from the analyst, review, and standards sources LLMs trust — and we tie that visibility back to CRM-tracked revenue. Over nine years we've done this for 60+ B2B tech companies, cybersecurity firms among them, with an 80% AI Search recommendation success rate.
We define and prioritize the commercial security prompts that actually decide deals in your category, then baseline where you stand on each across ChatGPT, Claude, Perplexity, Gemini, and AI Overviews. Because accuracy is a disqualifier in security, we don't just check whether you're named — we check whether you're framed in the right threat and compliance category and whether the model's facts about your certifications and posture are correct. You get an honest map of which answers you win, which you lose, where you're mis-categorized, and which hallucinations are working against you.
We line your visibility up against the citation and recommendation patterns we've seen across 60+ B2B tech companies, cybersecurity firms included. That tells us fast whether the gap is hype claims a model won't endorse, missing analyst or review-site signal, wrong category and compliance framing, or absent verifiable entity data — so we diagnose the cause specific to security instead of guessing at generic GEO tactics.
We pick the smallest set of moves that will actually change answers for your security prompts: rewriting claims into defensible, citable language; earning the analyst-adjacent and review-site mentions models trust in this category; correcting entity, schema, and compliance signals; or publishing the comparison and compliance-mapping content that becomes a cited source. No fluff retainer — only the levers that shift recommendations toward pipeline that survives review.
We execute the chosen path as a repeatable program: claim pressure-testing, category and compliance entity cleanup, verifiable trust-fact seeding, answer-shaping content with real technical depth, analyst-adjacent and review-signal work — sequenced so each piece corroborates the others. In a category where one weak signal undermines trust, the goal is an AI presence that compounds defensibly, not a one-off experiment.
Every cycle we re-measure the prompt set, re-check for new mis-categorization or hallucinations, attribute AI-sourced leads in your CRM, and pull your sales team's read on which conversations cleared a security review. Prompts and verticals that produce qualified pipeline get more investment; the ones that stall get cut. The system tunes toward tracked SQLs and revenue through security review, month over month.
After nine years marketing to technical and executive buyers across 60+ B2B tech companies, cybersecurity firms among them, we already know which commercial security prompts decide deals — the "best [category] for [vertical]," "SOC 2-ready alternatives to [vendor]," and "is [vendor] [framework] authorized" questions — and which sources a model trusts to answer them in this category. We don't start by guessing your prompt set or learning the difference between EDR, XDR, SIEM, and SOAR on your budget; we start from a library of security prompts and citation patterns we've watched convert into pipeline, so your program skips the expensive discovery a generic GEO retainer bills for.
Most security teams can't say whether a model recommends them, ignores them, or — worse — describes them in the wrong threat category or with the wrong compliance scope. We baseline your presence across a defined commercial security prompt set on day one across ChatGPT, Claude, Perplexity, Gemini, and AI Overviews: who gets named, who gets cited, where you're mis-categorized, and where the answer about your certifications or posture is simply wrong. Within the first weeks you know exactly which answers you're losing and why — including the hallucinations actively working against you — instead of running months of blind experiments.
AI visibility in security isn't a standalone trick; it shares a foundation with analyst relations, review-site presence, PR, and SEO. We decide where the leverage actually sits for your category — sometimes it's earning a credible third-party mention or review the model already cites, sometimes it's correcting entity and schema data so your certifications and category are machine-readable and unambiguous, sometimes it's a comparison or compliance-mapping page that becomes the source a model quotes. We only fund the moves that change the answer for a security buyer, not a fixed checklist of GEO busywork.
Recommendations only matter if the buyers they send survive your own sales process — and in security that process is a gauntlet of review rooms. We sit close to your sales team, review the AI-sourced leads they actually take meetings with, and learn which prompts and framings produce conversations that clear a security review versus tire-kickers and researchers. That feedback retargets the prompt set every month — toward the verticals, compliance regimes, and comparisons your team can win — so the program optimizes for deals that close, not raw mention counts that stall in vendor risk assessment.
We treat AI Search like a measurable channel end to end, tracked through the stages security deals actually stall in. Beyond a visibility report, we instrument how AI-discovered prospects enter your CRM and tie movement on the prompt set to tracked MQLs, SQLs, and revenue through vendor risk assessment and security review to closed-won. You see the line from "now recommended for this prompt" to "deal that cleared security review" — the same CRM discipline behind the $30M+ in marketing-led revenue we've tracked for clients.
Strategy first, channels second, sales feedback always. We measure by the qualified demand and revenue we can trace back inside the CRM.
Our founder led their marketing as Head of Marketing, running a 7-person team across SEO, demand gen, and paid — $1M/year in marketing revenue. Re-engaged in 2026 and halved cost per MQL and SQL.
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Rebuilt inbound from scratch — 100% YoY SQL growth, 207% more traffic, domain rating from 12 to 45, and 141 articles shipped.
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“We've seen a 207% increase in web traffic and our domain rating improved from 12 to 45.”
Took a referral-only firm to a real new-business engine — 5 deals and $240K revenue from Meta in a year, plus 2–4 SQLs/month from ChatGPT.
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Positioned them as the recommended Salesforce partner inside the major LLMs — two $1M enterprise deals closed from ChatGPT in a 3-month engagement.
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Positioned a small studio in a niche AI-search category — recommended by LLMs for 2 commercial keywords, landing a big client in one month.
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Recommended by every major AI assistant for 3 commercial keywords, plus a deal closed from Meta appointment funnels.
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Positioned for an important commercial keyword and recommended by the major AI assistants within a single month.
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Took over marketing as their agency after an unsuccessful in-house run — GTM strategy, paid funnels, AI search, and LinkedIn delivered 75 leads and the first qualified pipeline.
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A targeted ABM campaign booked 13 conversations with ICP accounts and opened three opportunities in two months.
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A custom AI-assistant paid funnel produced 76 leads and 34 booked appointments at $123 per call with an ICP.
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Meta appointment funnels delivered 35 leads at a $42 CPL and closed two deals on modest spend.
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Thanks to XQL Group's efforts, we've seen a 207% increase in web traffic and an improvement in domain rating from 12 to 45. The team has successfully optimized our SEO strategy and gained around 160 backlinks. Overall, they're responsive and thorough in their project management.
Since working with XQL Group, our domain rating has improved from 27 to 44. In addition, we've seen a 15% increase in monthly traffic within nine months. The team completes work on time and within the agreed budget. Moreover, their subject matter expertise is highly impressive.
XQL Group's efforts have resulted in 44 leads from paid campaigns and improved web traffic from Germany by 5x. The team is responsive, quickly surfaces issues, and communicates regularly through chats and virtual meetings. Their expertise and proactiveness have impressed our team.
Organic traffic has increased by 10–15% each month, and we have started receiving our first inbound requests. XQL Group's optimization tips have also helped improve keyword rankings, and internal stakeholders are impressed with the team's collaborative approach.
XQL Group has successfully defined a clear marketing strategy and established our company's unique value proposition. The team has also helped hire critical specialists for our marketing team. They are communicative and organized, and their expertise in the tech industry is impressive.
Thanks to XQL Group's efforts, we have defined our marketing strategy and hired key developers for our website. The team has launched retargeting campaigns on LinkedIn and developed a strong content marketing strategy. XQL Group's marketing expertise is a hallmark of the engagement.
They were not just talking about AI search in theory; they knew how to approach it practically.
What impressed us most was their deep specialization in working with software development companies.
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They operated with the discipline and initiative of an internal senior marketer.
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The discipline is the same — getting recommended inside AI answers — but security raises the bar in three ways generic GEO ignores. Models are more cautious about endorsing a security vendor, so the absolutist claims that fill security marketing ("unbreachable," "100% protection") are exactly what a model hedges around; the vendors that get named have precise, evidenced claims. Category accuracy is make-or-break: being filed as antivirus when you sell EDR, or with the wrong compliance scope, drops you from the prompts you can win. And the answer about you has to be factually correct — a model can hallucinate your certification status or posture. We optimize the prompts, claims, category framing, and citation sources specific to security, not just your website.
We optimize for ChatGPT, Claude, Perplexity, Google Gemini, and Google's AI Overviews — the surfaces where security leaders now build shortlists. The prompts that matter are the commercial ones a buyer runs mid-evaluation: "best [category] for [vertical]" (e.g. best EDR for healthcare), "SOC 2-ready alternatives to [vendor]," "is [vendor] FedRAMP authorized," "[category] vs [category]," and post-breach "alternatives to [vendor]" searches. Because these models draw on overlapping signals, a well-built program lifts visibility across all of them, but we baseline and track each engine separately so you can see where you're strong and where there's still a gap or a mis-description.
We do four things at once, because in security all four gate the recommendation. We rewrite claims into precise, qualified, evidenced language a model will repeat instead of hedge around. We correct category and compliance framing — entity, schema, and on-site signals — so the model places you in the right security category and attributes the right compliance scope. We earn mentions and reviews on the sources models trust in this niche: analyst-adjacent coverage, G2 and Peerspot, standards references, and credible security publications. And we seed verifiable entity facts — certifications, data residency, audit summaries — so the model's answer about your posture is correct. Tweaking your own site alone rarely moves a security recommendation; the external signal, the claims, and the entity accuracy are what shift the answer.
That's one of the most damaging and most overlooked problems in security AI visibility, and it's silent — you lose deals without ever seeing the conversation. If a model has you as antivirus when you're EDR/XDR, or says you're SOC 2 when the buyer needs FedRAMP (or worse, states a certification you don't hold or a breach you never had), you fail the prompt that matters and create a trust problem when reality doesn't match. Our baseline audit explicitly checks for mis-categorization and factual errors across every engine. We then fix the upstream causes — ambiguous entity and schema data, unclear category and compliance signals on your own properties, and incorrect or stale third-party sources — and seed clean, machine-readable trust facts so the model has an authoritative source to draw the correct answer from.
It would if you did it the way most GEO is done — and that's precisely why our approach is the opposite. Models that are cautious about security don't reward louder claims; they reward corroborated, qualified ones. So the work pushes toward precision, not hype: we pressure-test efficacy language so it's defensible, anchor it to evidence a model can find (independent tests, audits, named outcomes), and make sure the third-party sources the model cites say the same thing. The result is an AI presence that survives the CISO and legal scrutiny a recommendation triggers, rather than copy that earns a mention and then collapses under review.
We baseline your presence across a defined commercial security prompt set — who's named, who's cited, how you're categorized, and whether the facts are right — then track movement on that set over time. The part most agencies skip: we instrument how AI-discovered prospects enter your CRM and track them through the stages security deals stall in — vendor risk assessment, security review, procurement — tying prompt-set movement to MQLs, SQLs, and closed-won. You see the line from "now recommended for this prompt" to "deal that cleared security review," the same CRM discipline behind the $30M+ in marketing-led revenue we've tracked for clients.
It depends on your starting authority, how accurate your current AI footprint is, and how competitive your category prompts are — but AI Search can move faster than classic SEO because a few credible new signals, a claim rewrite, or an entity correction can change an answer quickly. Correcting an outright mis-categorization or hallucination is often the fastest win. We've seen B2B tech clients earn their first LLM-sourced inbound within the first month or two, and our work runs at an 80% recommendation success rate across targeted commercial prompts. Defensible category positions — where you're consistently and accurately recommended across many security prompts — compound over the following quarters as analyst, review, and citation signal accumulates.
Yes — they reinforce each other, and in security both matter. Much of the content, entity data, and authority that ranks you in organic search is the same signal models read when they build recommendations, and AI Overviews sit directly on top of search results. The compliance-mapping and comparison content that ranks for a security buyer also tends to be the content a model cites to answer their prompt. We don't treat AI Search as a replacement for SEO; we run them as one system, which is why this page links to our B2B SEO service. Many security clients run both, with the cybersecurity industry page laying out how the full growth stack sequences together.
Bring your offer, channels, and revenue goals. We'll show you where the biggest growth constraint is and what to build next.
For B2B tech companies selling complex expertise to serious buyers.
I’m Danylo, founder of XQL. For 9+ years I’ve helped B2B tech companies turn technical expertise into pipeline — 60+ clients and $30M+ in CRM-tracked revenue.
30 minutes, no deck. Bring your offer, channels, and revenue goals — I’ll come with a read on where your biggest growth constraint is and what to build next.
