Are You Conquesting or Defending? How to Choose the Right GEO Tool.

Vercel grew ChatGPT referrals from less than 1% to 10% of new signups in six months. That’s not impressions. That’s signups. Tally, the form builder, saw AI search become its biggest acquisition channel, helping it grow from $2 million to $3 million ARR in four months, with over 2,000 new users per week coming from AI platforms.

Now go to tryprofound.com. Look at the client logos: Ramp, DocuSign, Indeed, MongoDB. Go to xfunnel.ai: Monday.com, HubSpot, Wix, Getty Images.

These are two completely different use cases for AI search. Vercel and Tally are conquesting. They’re using AI search as a direct acquisition channel. People ask a question, the AI recommends them, and they get a signup. DocuSign, HubSpot, and Monday.com are defending. They already own their categories. They’re monitoring to make sure nobody takes ground.

The question you need to answer before you buy any GEO tool: which one are you?

Nobody Is Searching “What’s the Best E-Signature Service?”

Think about who’s actually asking AI engines for product recommendations. Nobody opens ChatGPT and types “what’s the best e-signature service?” and discovers DocuSign for the first time. DocuSign is the verb. They already won. The query that matters for DocuSign’s category isn’t “best e-signature”, it’s “what’s cheaper than DocuSign?” or “DocuSign alternatives for small business.”

The same is true for HubSpot. Nobody discovers HubSpot through an AI search. The dangerous queries for HubSpot are “HubSpot alternatives” and “CRM that’s easier than HubSpot.” These are defensive queries. The category leader needs to know when a challenger starts appearing in those answers.

This is why monitoring tools are built for category leaders. The value prop is defense: protect your visibility, track competitive threats, detect when someone gains ground. Go read the homepages. Profound: “analyze how brands are represented across AI search engines.” XFunnel: “measure, analyze, take action and experiment to improve your visibility.” These are defensive positioning statements for companies that already have visibility.

Now think about Vercel. Nobody was searching “what’s the best deployment platform?” before Vercel optimized for AI search. The queries that drove Vercel’s growth were “how should I deploy my website?” and “best hosting for Next.js apps.” These are conquesting queries. The person asking doesn’t have a preferred vendor yet. They’re in discovery mode. The AI’s recommendation is the first brand they encounter.

Vercel’s 10% of signups came from the last two query types. Tally’s growth came from appearing when people asked “best free form builders” and “Jotform alternatives.” These are acquisition queries, not brand awareness queries. The AI isn’t confirming a decision the buyer already made. It’s making the introduction.

The Logos Tell You Who the Product Was Built For

Profound was founded in 2024 and raised $58.5 million, including a $35 million Series B led by Sequoia Capital, with Kleiner Perkins and Khosla Ventures, according to Fortune. By August 2025, over 2,000 marketers from 500+ organizations were using it daily. The named clients include Ramp, DocuSign, Indeed, MongoDB, and Chime.

XFunnel was also founded in 2024 and was acquired by HubSpot in October 2025, according to Tracxn. At the time, it had approximately 12 employees. Its client logos include Monday.com, HubSpot, Wix, Getty Images, and LastPass.

Sequoia’s portfolio includes Ramp and has deep connections to MongoDB and the broader enterprise SaaS ecosystem. When a Sequoia-backed startup launches, the firm’s network provides warm introductions to portfolio companies. This is standard enterprise SaaS distribution: the VC’s network seeds the first customers, which creates the logo wall, which attracts similar enterprise buyers. It’s effective go-to-market. It also means the product was designed for that buyer profile from day one.

Every company on those logo walls shares the same profile:

• Category leader with 80+ domain authority

• 1,000+ published articles

• Marketing budget above $50 million annually

• 20+ person content team or agency on retainer

• Already cited in 40%+ of their category’s AI queries

• Measures success in impressions and share of voice, not signups from AI referrals

I’ve spoken with marketing leaders at organizations this size. They measure their $50–$100 million annual marketing budget’s success on two metrics: did they fulfill their spend requirements, and did they deliver the target number of impressions? That’s a fundamentally different measurement framework than what Vercel and Tally care about. Vercel tracks signups. Tally tracks ARR growth. These companies measure AI search by whether it produces customers, not whether it produces data points.

Data Businesses vs Outcome Businesses

Profound processes over 1.5 billion prompts per day across AI platforms, according to its website. That’s an extraordinary amount of compute. Building and operating infrastructure to run billions of prompts daily, store the results, analyze citation patterns, track sentiment, and measure share of voice across multiple models costs millions of dollars per year.

That cost structure is the business model. When your primary expense is compute for data collection, your product is data. You’re Nielsen. You’re Neustar. You’re IRI. These are legitimate, valuable businesses. Nielsen sells consumer purchase data to Procter & Gamble, not to a DTC startup with 5 employees. The data is genuinely valuable at enterprise scale where the buyer has the operational infrastructure and the team capacity to act on it.

The monitoring tool replaces 5 seconds of manual work per query: instead of typing a prompt into ChatGPT yourself and reading the answer, the tool does it for you across hundreds of prompts. At enterprise scale with 500+ monitored prompts across multiple platforms, that automation is valuable. At startup scale with 50 prompts, you could do it manually in a morning.

The execution tool replaces 800 hours per quarter of content work: auditing, researching, restructuring, writing, publishing, refreshing. No amount of manual effort replicates that. The bottleneck for a conquesting company isn’t data about where they’re invisible. It’s the operational capacity to become visible.

Configurable Workflows vs Autonomous Ones

To their credit, Profound has moved beyond pure monitoring. Their platform now includes content agents that generate optimized drafts and publish directly to your CMS with human-in-the-loop approvals. Pre-built templates cover content refresh, FAQ generation, competitive research, and net-new creation. You can also build custom workflows with a drag-and-drop builder. XFunnel advertises “Content Briefs That Ship” and “Tailored Playbooks” with dedicated analyst support.

This is a meaningful evolution. But there’s a distinction between configurable and autonomous that matters at startup scale.

Profound’s model requires someone to set up and manage the workflows. You select templates, configure triggers, customize the agent’s scope, build team-specific pipelines, review each run, and approve before publish. This is powerful for enterprise teams that want granular control and have dedicated content ops staff to manage the system.

The question for a Series B company: who does the configuring? Who maintains the templates? Who monitors the agent runs and adjusts when citation behavior changes? At DocuSign, that’s a dedicated content operations role. At a startup, it’s the same person writing blog posts, running email campaigns, and building sales decks.

An autonomous model works differently. Agents run the full cycle (audit → gap identification → research → write → restructure → publish) without requiring manual workflow setup. The human touchpoint is review and approval of output, not design and maintenance of the pipeline. Both models produce content and publish to a CMS. The difference is operational overhead: who builds the system versus who reviews the output.

Meanwhile, Vercel didn’t use either model to grow from 1% to 10% of signups. They published their entire LLM SEO playbook, optimized their documentation for static HTML crawlability, structured content for semantic clarity, and kept everything fresh on 30/90/180-day review cycles. They executed first and measured second. The measurement confirmed what the signups already told them.

The Conquest Playbook Is Different

Companies using AI search for acquisition share a pattern that looks nothing like the monitoring workflow:

Vercel: Made documentation static and crawlable. Structured content for AI retrieval. Published their playbook publicly. Tracked signups, not share of voice. Result: 10% of new signups from ChatGPT (Vercel blog, April 2025).

Tally: Created comprehensive comparison content. “Best Free Online Form Builders.” “Jotform Alternatives.” Showed up where people already talked (Reddit, forums). Answered questions without selling. Result: AI search became their #1 acquisition channel. $2M to $3M ARR in four months (Vercel blog, April 2025).

Ahrefs: AI traffic drove 12.1% more signups despite making up only 0.5% of all visitors. The conversion rate from AI referrals dramatically outperformed organic (Ahrefs, June 2025).

None of these companies started with monitoring. They started with execution: publish content structured for AI retrieval, measure whether it produces signups, iterate on what works. Monitoring confirmed results that were already visible in the signup data.

The conquest playbook is: publish → measure signups → iterate → publish more. The defense playbook is: monitor → identify threats → brief the team → execute → monitor again. Both are valid. They serve different goals for different companies at different stages.

The Volume Economics of Conquest vs Defense

The conquest loop requires volume. You publish 20 articles, see which 5 get cited, restructure the 15 that didn’t, publish 20 more. You’re running experiments, not executing a quarterly content calendar. The speed of the loop determines how fast you learn which content earns citations.

Profound’s Growth plan costs $399 per month and includes 6 optimized articles. Their Enterprise plan is custom pricing with tailored packages. At 6 articles per month, you publish 72 articles per year. That’s enough for a defense cadence: update a few key pages, publish a handful of strategic pieces, monitor the results.

It’s not enough for a conquest cadence. If you’re building visibility from zero across 50+ uncovered prompts, 6 articles per month means full coverage takes over 8 months, assuming every article lands and none need revision. In practice, half will need restructuring after the first monitoring cycle. At 6 per month, you’re iterating slower than the citation landscape changes.

The volume math looks different at execution-first pricing:

The 27x volume difference at a lower price point isn’t a marginal advantage. It’s a different operating model. At 6 articles per month, you’re planning which articles to write. At 166 per month, you’re publishing, monitoring what gets cited, and iterating in the same week. The publish-monitor-revise loop that Vercel and Tally used to grow doesn’t function at 6 articles per month. It requires the velocity to test, fail, learn, and test again before the citation landscape shifts.

This also matters for content freshness. 40 to 60% of domains cited in AI responses change month-to-month, with drift reaching 70 to 90% over six months (Profound, 2026). If you have 200 published articles and need to refresh them quarterly, 6 articles per month covers 18 refreshes per quarter. That’s 9% of your library. The other 91% goes stale. At 166 per month, you can refresh your entire library and publish new content simultaneously.

How to Choose Between a Conquest and Defense Tool

The decision starts with diagnosing which mode the business is actually in, not which mode sounds better in a board deck. A Series B SaaS with $5M ARR is almost always conquesting even if leadership wants to talk about share of voice. A category leader with $500M ARR is almost always defending even if they run a conquest campaign on a new product line.

• If your primary KPI is signups or pipeline, prioritize execution volume over monitoring breadth. The publish-monitor-revise loop only functions at 50+ articles per month. Anything slower and the citation landscape shifts faster than you iterate.

• If your primary KPI is share of voice or sentiment across hundreds of tracked prompts, prioritize monitoring depth. Enterprise dashboards with competitive benchmarking and sentiment analysis are the right fit when the content ops team already exists to act on the data.

• If your team has a dedicated content ops role, configurable workflows are viable. Enterprise tools that require template setup and workflow maintenance assume someone owns the operational overhead.

• If your team is three people doing blog, email, and sales enablement, autonomous execution is the only viable path. Configurable workflows become shelfware when nobody has bandwidth to configure them.

• If your CPC data shows 10 to 15 high-intent queries driving conversion, tool breadth does not matter. Depth on those queries does. The Res AI 113-keyword ChatGPT validation study (2026) found 100% product recommendation rates on high-intent queries and 39% on not-capturable ones. Winning the 15 queries that matter beats appearing in 10,000 that do not.

• If you are replacing a manual content program, optimize for cost per published article, not cost per monitored prompt. A $399/month plan that publishes 6 articles costs roughly $66 per article. A plan that publishes 166 articles costs under $2 each. The unit economics determine whether the conquest loop is affordable at all.

The decision is not which vendor is best. The decision is which side of the conquest-versus-defense line the business is on, then which delivery model matches that side.

Frequently Asked Questions

How do I know if my company is conquesting or defending?

Look at the queries that drive your pipeline. If your top converting queries are category leader names or their alternatives (“HubSpot alternatives,” “cheaper than Salesforce”), you are probably defending an existing position. If they are problem-first queries (“how do I deploy a web app,” “best free form builder”), you are conquesting. A quick tell is whether the sales team talks about competitive displacement or category creation.

Can a category leader also run a conquest program for a new product line?

Yes, and they should. The conquest playbook applies to any line of business where the buyer does not yet have a preferred vendor. A $500M ARR company launching an adjacent product is, for that product, a conquester. The tooling choice follows the product’s stage, not the parent company’s size.

Why does monitoring not help conquest companies as much as it helps defenders?

Conquest companies usually start from zero visibility, which means their dashboard mostly reports absences. A monitoring tool that says “you appear in 3% of tracked prompts” tells a defender that a threat is emerging and tells a conquester something they already know. The data becomes useful only once execution has created enough signal for monitoring to inform iteration.

Is 6 articles a month ever enough for GEO?

For tightly scoped defense work on a small number of category-defining pages, yes. For any program trying to cover more than 20 to 30 buyer queries, no. The Res AI 852-article B2B citation structure study (2026) found a 3,500-word structural floor and Q4 word count quartile scoring 4.5x the structural total of Q1. You cannot hit that floor with 6 articles while also refreshing existing content on a 13-week cycle.

How does Vercel’s model scale to a company without an engineering culture?

Vercel made documentation static, crawlable, and structured, then published a playbook describing the tactics. A marketing team without an engineering culture can copy the structural tactics (static HTML, answer capsules, attributed stats, comparison tables) without copying the implementation details. What does not copy is the execution discipline. Vercel shipped continuously and measured signups, not impressions.

Why are Profound’s clients almost all category leaders?

Enterprise data platforms are sold through venture capital portfolio networks. Sequoia-backed Profound raised $58.5M and was seeded by introductions to similar-sized portfolio companies (Fortune, August 2025). The resulting client base is a selection effect of the distribution channel, not a conscious strategy to exclude smaller teams. It still tells you who the product was designed for.

Can execution-first tools also handle monitoring?

Yes. Execution-first platforms monitor the subset of queries they actually publish against, which is usually the 50 to 200 buyer queries the paid team already knows converts. They skip the 10,000-prompt dashboard because the incremental signal is negligible once the core query set is covered. Monitoring becomes the feedback loop that informs the next publish, not a separate product.

Does the conquest playbook work for companies outside SaaS?

Yes, with adjustments. Any category where the buyer has no preferred vendor and uses AI for discovery is conquest territory. The mechanics (structured content, attributed stats, comparison tables, freshness) are category-agnostic. The Vercel and Tally examples are SaaS because that is where the earliest data exists, but the Res AI 1,000-query Perplexity study (2026) showed the same pattern across ten verticals.

What is the realistic time to first conquest citation?

Four to eight weeks from first publish for a fresh domain in a competitive category. The 852-article structure study found listicles averaging 11.71 structural elements earning the fastest citations, compared to 5.51 for comparison pages and 5.38 for opinion essays (Res AI, 2026). Faster time-to-citation correlates with structural density, not domain authority.

What should a company do if it thinks it is conquesting but the data says it is defending?

Trust the data. If your top 15 converting queries include any existing brand name, you are at least partially defending. The action is to split the program: run a defense workflow on the category leader queries and a conquest workflow on the open-position queries. Tool selection follows the split, not the self-description.

Res AI is the conquest tool. Autonomous agents connect to your CMS, audit your content, restructure it for AI extraction, generate new articles for uncovered prompts, and publish directly. No workflow builder to configure. No templates to maintain. The agents run the full cycle. You review the output and add the editorial voice. The product is the citation and the signup that follows, not the dashboard.

See how it works →