Before an enterprise buying committee holds its first evaluation call, someone — an analyst, an IT director, a CPO — asks ChatGPT, Claude, Perplexity, Gemini, or an AI Overview: 'best enterprise [category] platforms,' 'top alternatives to [incumbent],' 'which [platform] vendors can handle [scale / compliance / integration].' Three to five names come back, and the shortlist forms before your AE has been looped in. We get your platform into that answer for the committee-consensus, displacement, and compliance prompts that precede a real evaluation — framed as a credible, enterprise-validated option, not an unknown challenger — and we tie recommendation visibility back to CRM-tracked pipeline and closed-won. Across our work we reach an 80% AI Search recommendation success rate on targeted commercial prompts.
We define and prioritize the category, displacement, and compliance prompts that precede evaluations in your specific enterprise market, then baseline where you stand across ChatGPT, Claude, Perplexity, Gemini, and AI Overviews. Because framing and accuracy are disqualifiers here, we do not just check whether you are named — we check whether you are framed as a credible enterprise option or collapsed into feature parity, whether the model defaults the buyer to the incumbent or the analyst-darling, and whether the model's facts about your scale, certifications, integrations, and category are accurate. You get an honest map of which shortlist formations you win, which you lose, where you are commoditized, and which mis-framings are working against you.
We benchmark your visibility and evidence base against the citation and recommendation patterns we have seen across 60+ B2B tech companies, enterprise platform vendors included. That tells us fast whether the gap is missing outcome-led enterprise case proof at comparable scale and vertical, a thin G2/TrustRadius Enterprise-grid or peer-review footprint, weak entity data, wrong compliance or integration association, or absent business-case and total-cost-of-ownership narrative the model needs to recommend a challenger over the incumbent — so we diagnose the cause specific to this analyst-gated, procurement-gated market instead of guessing at generic GEO tactics.
We pick the smallest set of moves that will actually change answers for your enterprise prompts: earning placement on the G2 and TrustRadius Enterprise-grid surfaces, comparison pages, and peer-review publications models cite for vendor shortlists; correcting entity and trust data so you are tied to the right enterprise scale, compliance posture, and category framing; or publishing the displacement, compliance-depth, and 'how to evaluate' content that becomes a citable source for a risk-averse committee. No fluff retainer — only the levers that shift recommendations toward booked, qualified enterprise conversations.
We execute the chosen path as a repeatable program: G2/TrustRadius Enterprise-grid and peer-review-signal work, entity and schema cleanup that fixes your scale, compliance, and category framing, verifiable enterprise trust-fact seeding (named references, compliance certs, ROI data, integration depth), outcome-led case studies reworked as citable proof by vertical and enterprise scale, and answer-shaping displacement, compliance, and evaluation-guide content — sequenced so each piece reinforces the others. In a market where one weak signal confirms the 'too small' or 'mid-market only' suspicion and the incumbent's brand is always the easy default, AI Search becomes a compounding, defensible asset.
Every cycle we re-measure the prompt set, re-check for new feature-parity collapses or incumbent-default answers, attribute AI-sourced leads through your enterprise sales cycle in the CRM, and pull your sales team's read on which conversations qualified as real RFP-stage or vendor-selection opportunities versus researchers and students. Prompts that produce enterprise pipeline that survives procurement and InfoSec get more investment; the ones that pull mid-market or curiosity-led traffic get cut. The system tunes toward tracked SQLs and closed-won deals, quarter over quarter.
We have spent 9+ years marketing for 60+ B2B tech companies, including platforms and vendors sold into large enterprises with committee-driven, procurement-gated buying cycles. We already know the commercial prompts that precede an enterprise evaluation and how they fork by category, vertical, compliance requirement, and integration stack. 'Enterprise CRM' pulls analysts and students benchmarking vendors; 'best enterprise CRM for manufacturing with SAP integration and SOC 2' pulls a CIO or VP of Sales mid-evaluation with a board-approved budget and a named incumbent to displace. We start from the prompt set we have watched convert to discovery calls and RFPs for enterprise platforms — not a content strategy built from scratch on your budget.
Most enterprise software teams cannot say whether a model names them on the category-level and displacement prompts that matter, mis-frames them as a mid-market vendor, attributes the wrong compliance posture or integration story, or simply defaults the buyer to the category-leader or analyst-darling and skips challengers entirely. We baseline your presence across the committee-consensus, displacement, and compliance prompts relevant to your category on day one across ChatGPT, Claude, Perplexity, Gemini, and AI Overviews: who is named, who is cited, where you are flattened into feature parity, and where the model's facts about your scale, certifications, or category are wrong. Within weeks you know exactly which shortlist formations you are absent from and why.
For an enterprise software vendor, the lever that moves an AI answer is rarely your own features page — it is the signal a model already trusts for enterprise validation: G2 Enterprise-grid reviews and named enterprise references at comparable scale and vertical, presence in analyst-adjacent and peer-review surfaces, the comparison and 'alternatives to [incumbent]' pages a model treats as independent shortlist-formation evidence, and clean entity data that ties your platform to a specific category, integration story, compliance posture, and enterprise scale. We fund only the moves that shift recommendations toward a credible-challenger or validated-enterprise framing for your category, not a generic content checklist that ignores how committee-driven, analyst-gated enterprise markets actually get cited.
An AI recommendation is worthless if it sends junior analysts, students writing case studies, or curious mid-market prospects without board-approved budget. We work close to your enterprise sales team, review which AI-sourced leads reached discovery or qualified as RFP-stage opportunities versus researcher inquiries, and retarget the prompt set monthly toward the categories, verticals, and compliance profiles your AEs actually close — so the program optimizes for conversations that survive procurement and legal, not raw recommendation counts.
We treat AI Search as a measurable channel for a long, committee- and procurement-gated enterprise cycle. Beyond a visibility report, we instrument how an AI-discovered prospect enters your CRM and tie prompt-set movement to qualified meetings, SQLs, and closed-won deals — with security review, vendor risk assessment, and procurement tracked as their own pipeline stages, because that is where enterprise software contracts stall for quarters. You see the line from 'now cited as a credible alternative to [incumbent] for [vertical]' to 'closed-won deal that cleared InfoSec and procurement,' the same CRM discipline behind the $30M+ in marketing-led revenue we have 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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Senior operators on every account. Never a junior pod.
Rebuilt inbound from scratch — 100% YoY SQL growth, 207% more traffic, domain rating from 12 to 45, and 141 articles shipped.
ServicesFractional CMO · SEO · Demand Generation
“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.
ServicesMeta Ads · Fractional CMO · AI Search
“Their expertise and proactiveness have impressed our team.”
Positioned them as the recommended Salesforce partner inside the major LLMs — two $1M enterprise deals closed from ChatGPT in a 3-month engagement.
ServicesAI Search · Fractional CMO · Meta Ads
“They operated with the discipline and initiative of an internal senior marketer.”
Built the marketing function from zero — website, SEO, paid, AI search — from 166 to 2,513 monthly clicks and 3 enterprise deals won.
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“They defined a clear marketing strategy and established our unique value proposition.”
Turned Salesforce-niche SEO into a deal channel — 2.73× traffic and MQL-to-SQL conversion up from 17% to 29%.
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“We have started receiving our first inbound requests.”
Restructured the marketing team and shifted to lead-driving SEO — doubling client count and growing SQLs 133% in nine months.
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Switched them from brand-awareness content to lead-driving SEO — 20 of 25 service pages ranked top 5 and the first deals closed from organic.
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“XQL Group's marketing expertise is a hallmark of the engagement.”
Stood up SEO as a new acquisition channel — domain rating 27 to 44, 50+ leads, and 88 articles in nine months.
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“Their subject matter expertise is highly impressive.”
After six months of zero results from another agency, we delivered +70% Google clicks and grew AI-assistant traffic from 10 to 154/month in three months — and the first SQLs from SEO.
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Meta appointment funnels at scale — 466 leads and over 100 booked sales conversations from a single paid channel.
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“They've brought structure, strong execution, and constant initiative.”
Set up marketing from zero — positioning, website, LinkedIn social selling, and an ABM campaign that landed four new clients fast.
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“Their ability to combine strategic vision with hands-on execution was particularly valuable.”
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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“They knew how to approach AI search practically, not just in theory.”
Recommended by every major AI assistant for 3 commercial keywords, plus a deal closed from Meta appointment funnels.
ServicesAI Search · Appointment Funnels
“What impressed us most was their deep specialization with software development companies.”
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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Repositioned the agency, revamped the site, and built appointment funnels that booked qualified intro calls at just $34 each.
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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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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.
They've brought structure, strong execution, and constant initiative to improve outcomes.
They operated with the discipline and initiative of an internal senior marketer.
Their ability to combine strategic vision with hands-on execution was particularly valuable.
Their focus on results and true interest in making things work set them apart.
XQL Group's project management was exemplary.
The quality of their work is consistently high.
Not from how polished your product page is, and not from your analyst quadrant position alone — the model builds its answer from how often and how credibly your platform is discussed across sources it treats as independent validation: G2 and TrustRadius Enterprise-grid reviews and named enterprise references, peer-review and analyst-adjacent publications, and 'alternatives to [incumbent]' and 'top [category] vendors' comparison pages. In a market where every enterprise software site recites 'enterprise-grade, scalable, AI-powered,' that external signal and a clear, specific category framing — the right scale, compliance posture, integration story, and vertical — are what get you named as a validated option rather than collapsed into feature parity. We work on the prompts, the citation sources, and the entity and trust data together, because tweaking your own site almost never moves the recommendation in a market the model treats as analyst- and committee-gated.
That is the most common failure mode in enterprise software AI visibility, and it mirrors the analyst-gatekeeper ceiling you already compete against — now reproduced inside the generated answer. A model with no strong, independent signal for a challenger defaults to the best-documented name: the category-leader or the last Gartner Quadrant winner. We counter it the way you'd counter it in a real evaluation: by building the outcome-led, named enterprise proof at comparable scale and vertical that a model can cite as independent validation, earning the G2 Enterprise-grid and peer-review signal that sits alongside analyst notes in the sources models trust, and seeding displacement and 'alternatives to' content that positions your platform as the logical step after evaluating the incumbent. Our baseline audit explicitly flags every prompt where the model defaults to the category-leader or analyst-darling, so we know exactly which ones to target first.
That is the most damaging and most overlooked problem in enterprise software AI visibility, and it is silent — you lose shortlist positions without ever seeing the conversation. If a model calls you a mid-market vendor when you serve Global 2000 accounts, mis-states your compliance certifications (SOC 2, FedRAMP, ISO 27001), attributes the wrong integration story, or files you in a category you have evolved beyond, you fail the prompt a risk-averse committee uses to screen for safety and confirm you are enterprise-grade. Our baseline audit explicitly checks for these mis-framings and factual errors across every engine. We then fix the upstream causes — ambiguous entity and schema data, unclear scale and compliance signals on your own properties, and stale third-party sources — and seed clean, machine-readable trust facts so the model has an authoritative source for your correct enterprise profile.
AI Search shapes the mental model the committee forms before any member speaks to a vendor — which makes it one of the few channels that can reach stakeholders you never meet: the CFO who vetoes on TCO grounds, the InfoSec lead who screens on compliance, the technical evaluator who checks integration story, and the end-user sponsor who validates peer references. We target the prompts each committee role is most likely to run — 'enterprise [category] with [compliance] for [vertical],' '[platform] vs [incumbent] total cost of ownership,' 'how to evaluate enterprise [category] vendors,' '[platform] integration with [SAP / Salesforce / ServiceNow]' — so your platform shows up credibly across the evaluation, not just in the first 'what category platforms exist' search.
We baseline your presence across a defined category, displacement, and compliance prompt set — who is named, who is cited, how you are framed by scale and enterprise credibility — then track movement on that set over time across the five major assistants. The part most agencies skip: we instrument how AI-discovered prospects enter your CRM and follow them through the stages enterprise deals actually stall in — first touch, discovery call, SQL, and the security review, vendor risk assessment, and procurement stages each tracked as their own CRM status — through to closed-won. You see the line from 'now cited as a credible alternative to [incumbent] for [vertical]' to 'deal that cleared InfoSec and procurement,' the same CRM discipline behind the $30M+ in marketing-led revenue we have tracked for clients.
Alongside — they reinforce each other in ways that are especially high-leverage for enterprise. ABM concentrates paid and outbound spend on the named accounts that can write a seven-figure check and reaches the whole buying committee, as we engineered for Intelvision (28.9x ROAS, $240K revenue, 100 meetings booked from enterprise accounts). Revenue SEO around displacement, integration, and compliance themes builds the durable bottom-of-funnel base the model also reads, as it did for Synebo (500% more SQLs, 2.73x organic traffic). AI Search Optimization captures the buyers who now start their pre-vetting in an assistant and would otherwise never reach your site or your AEs — the committee members running shortlist-formation prompts before any RFP is drafted. The strongest enterprise software programs run all three as one system, sequenced so ABM fills qualified pipeline now while SEO and AI Search compound the authority that makes you harder to ignore every quarter.
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.
