B2B marketing agency: win the AI shortlist.

A B2B deal is settled by five to eleven stakeholders, and each one now opens ChatGPT or Perplexity and asks for a vendor shortlist before sales is ever contacted. That research happens in a chat window no analytics tool sees, so if the model does not name you, you are cut from deals you never knew existed. B2B GEO makes you the answer those engines return. We cover the distinct prompt every role types: the economic buyer asking about ROI, the technical evaluator asking about your API, procurement asking who clears the security review. You appear across all of them, not just the obvious category query.

Generic GEO chases a citation count for its own sake. B2B GEO maps your category's real prompts across all five engines, mines the committee's questions from sales calls and won-and-lost notes, and reverse-engineers the citation graph the models already use to build shortlists around your competitors. Then we fix the source pages and schema the engines lift from and earn the analyst and peer-review references B2B credibility rests on. The work is bespoke to what that research surfaces, because a developer-tool committee and a procurement-heavy enterprise committee cite different sources and weight the engines differently. Citation share by role is the input. Influenced pipeline is the number you report to the board.

A B2B purchase is not one anonymous visitor. It is a committee of five to eleven people researching independently in Google and in AI chat long before anyone fills a form, and each role wants something different. The economic buyer wants ROI and category framing, the technical evaluator wants documentation and integration detail, procurement wants comparison clarity and the security gates cleared, the end user wants workflow proof, and the internal champion needs proof points to sell the purchase upward. So the page set is wider and more bottom-weighted than a traditional program: your comparison, alternatives, category, and integration pages carry the deal, not a top-of-funnel blog post.

The difference from generic SEO is the source and the target. We brief those pages from sales calls, RFPs, and won-and-lost deal notes rather than a keyword tool, because nobody types a full RFP line into Google. And the same pages now have two jobs: ranking for the stakeholders who still search, and getting cited in the AI answer for the ones who ask instead. When a committee member asks an engine for a shortlist, the model assembles it from those exact comparison and category pages. If you are not on the list it returns, you are quietly cut from deals you never see. We measure the whole thing to influenced pipeline and deal velocity, not to sessions or a rankings dashboard.

A B2B purchase is decided by a committee of five to eleven people, each researching a different question before anyone fills a form. The content that wins answers those exact questions: comparison and alternatives content for procurement and the economic buyer, integration depth for the technical evaluator, use-case proof for the end user, and point-of-view thought leadership that builds the category case. Generic content chases keyword volume for one anonymous reader. B2B content is bottom-weighted and mapped to the roles who actually sign, with subject-matter-expert bylines where credibility matters.

The source is what sets it apart. Every engagement opens with a research sprint: we interview your sales reps and recent customers, pull the questions buyers ask from call recordings, read your win-loss interviews, and mine the RFP and RFI questions procurement sends. Most of those questions never appear in a keyword tool because no one types a full RFP line into a search box. Those same comparison, category, and POV pages now feed the answer ChatGPT and Google's AI Overview hand back, so an /alternatives page or a POV piece has to rank on Google and get cited in the AI shortlist. We refuse a fixed content calendar, because a playbook assumes every B2B company sells into the same committee with the same objections, which is never true. We report to influenced pipeline, not a publishing count.

Most B2B and SaaS sites bury deep product substance under a stack the engines cannot read: a JS-rendered app shell, gated docs, thin marketing pages sitting on top of rich detail, and no machine-readable structure. A human browser runs the JavaScript and sees the full page. Many crawlers and most LLM retrieval pipelines do not render reliably, so they index a near-empty frame where your product should be. When a buyer asks an engine for the best vendor in your category, the model builds its answer from pages it could actually read. If yours rendered blank, you are simply absent. This is the layer most programs only half-cover, and the one that unlocks everything content and citations are built on.

We fix four things. Render parity, so the substance inside your app shell, hydrated marketing pages, and single-page docs reaches the HTML crawlers and parsers receive. Crawl and index health: a clean sitemap, sane crawl budget, no orphaned integration pages. Core Web Vitals and INP on the templates buyers evaluate you on. And the AI-readability layer most teams skip entirely: llms.txt, parser-friendly semantic markup, and SoftwareApplication, Product, FAQPage, and Organization schema so engines can read, trust, and quote you. It starts with a custom crawl-and-render audit of your specific stack, never a generic checklist, because two SaaS sites on the same framework can fail in completely different ways. We fix in priority order by citation impact and report against pages that became readable, then became cited.

AI engines cite third-party sources far more than your own domain, and a B2B buying committee trusts them more too. The shortlist gets settled on review sites, analyst notes, trade editorial, and community threads, not on your marketing site. A model assembling a shortlist does what a careful buyer does: it discounts the source paid to praise itself. Your own domain is the least independent source on the internet about you, so engines weight it lightly and lean on the citation graph instead. Off-site content for B2B is the discipline of earning an accurate, favorable, well-cited presence on exactly the surfaces a stakeholder and an engine vet you on, so the third-party signal says yes.

This is earned and managed, not bought or claimed. The core surfaces are the review platforms where velocity and recency matter as much as star rating, because a stale profile reads as a fading vendor to both a human and a model. Around them sit analyst-adjacent notes, trade editorial that frames the category, and the community threads where buyers lurk before they ever talk to sales. We prompt genuine customers to leave honest reviews, correct inaccurate third-party listings, and contribute real expertise to the communities and editorial the engines actually cite. We never buy reviews or spray generic guest posts: platforms penalize fake reviews, and a thin post on an irrelevant blog is a citation no one pulls. Every engagement starts by mapping which surfaces the engines cite for your category, then earning presence on those specific ones.

Your buyers ask peers in r/sysadmin, r/devops, r/sales, and r/marketing before they trust a single word on your site. In B2B, a committee rarely takes a vendor's own claims at face value, so the technical evaluator, the practitioner, and the champion go ask peers in the subreddits for their category first. The engines have noticed: Google licenses Reddit data, ChatGPT leans on it heavily, and both quote those threads back when a committee asks for a shortlist. Reddit is where the unfiltered, peer-to-peer truth about your product lives, which makes it one of the highest-return B2B GEO surfaces. Winning those threads means winning a slice of the answer the buyer never sees you influence.

Drive-by promotion gets you banned, which is exactly why most vendors fail there. Bans come from astroturfing, sock-puppet networks, and low-effort promotional comments. We do the opposite. Every account we use has real history, age, and karma earned by genuinely participating, and we document and respect each subreddit's rules and mod culture before contributing. If a sub bans our category, we do not post there. There is no generic playbook of canned comments: a r/devops conversation and a r/sales conversation reward completely different things. Every engagement opens with a research sprint that finds the intersection of where your buyers actually ask questions and which threads the engines already pull into answers for your category. We measure against whether the Reddit evidence the model quotes back includes you, not vanity karma.

In a high-consideration B2B purchase, the citation graph around you, meaning who independently vouches for you and where, matters more than the copy on your own site, because both Google ranking and AI citation lean on third-party trust. Each stakeholder evaluating you checks different outside surfaces before anyone fills a form, and they trust independent sources far more than your marketing. So the link that matters in B2B is the analyst mention, the trade-press feature, the cited data study, the verified review, and the community thread, not a footer link on an unrelated blog. When a buyer asks an engine for the best vendors in a category, the model assembles its answer largely from those independent surfaces. If you are absent from the citation graph it reads, you are quietly cut from deals you never see.

We earn that authority instead of buying it. Digital PR that lands you in industry and trade publications, original data studies and proprietary survey research that journalists and analysts cite, presence on the review and analyst surfaces a committee checks, expert commentary placed through the platforms that replaced HARO, and the podcasts and community threads the engines pull from. We do not touch private blog networks, paid placements dressed up as editorial, or HARO spray-and-pray, because those inflate a link count without building the trust a committee or a model actually weighs. Every program starts with a research sprint that maps which third-party domains and threads the engines cite for your category and your named competitors, then earns placements on exactly those in priority order. We report on whether you are on the surfaces the committee and the models read, not a raw link count.