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There was a time when scaling your content strategy meant climbing the Google ladder: write great content, optimize for keywords, earn backlinks, and wait. That playbook still has value, but it’s no longer the whole story.

Today, buyers are skipping Google altogether. They’re asking LLMs like ChatGPT or Claude for recommendations. They’re relying on Perplexity to summarize competitive landscapes. They’re getting product suggestions directly from AI Overviews in Google search.
And here’s what might surprise you: that traffic converts better.

"We’re seeing LLM traffic convert at 30% versus 5% for traditional SEO," said Eric Hann, Head of Growth at Order.co. "It started as paranoia; we were just trying to make sure we weren’t losing traffic. But now it’s a core part of our strategy."

At Stage 2, we’ve heard similar stories across the portfolio. Founders are watching LLMs emerge as a new top-of-funnel driver, and scrambling to figure out what to do next. If that’s you, here’s a practical guide to breaking through the noise and getting started.

Before You Begin: Remember that Focus Matters

If you're a small team, you can’t afford to overhaul your SEO strategy every quarter. 

The fundamentals still matter:

• Write for humans first.
• Maintain clean site structure and page speed.
• Focus on helpful, intent-driven content.

But treat LLMs like a new distribution channel. Start small: pick a few LLM prompts that matter to your buyer, see how you show up, and test from there. While some features like AI Mode are still in Search Labs, Google’s AI Overviews have already started rolling out more broadly, meaning this shift is happening fast. Founders should hold onto their SEO fundamentals, but reserve time to test, learn, and iterate as this new layer of search evolves. This shift has sparked new terms like Generative Engine Optimization (GEO), content strategies designed for how LLMs surface answers, not just how search engines rank pages.

Step 1: Understand Where You Stand Today

Start by understanding how and where you're already showing up in LLM results.
There are a myriad of new tools that have popped up to help you get there. Peec AI or Profound are a couple of good options and they’ll help you track how your site is referenced across AI systems like ChatGPT, Perplexity, and Google’s AI Overviews. Think of a tool like Peec AI as the “SEMrush for LLMs", it lets you monitor prompt visibility, benchmark against competitors, and zero in on what content is breaking through.

Understanding where you’re showing up in LLM rankings is important, but how can you connect that to the traffic you’re actually seeing in your analytics?
It can still be a bit murky, but there is data to show you a signal of what’s working.

In Google Analytics, LLM-referred traffic can show up inconsistently. ChatGPT sometimes tags its referrals, allowing you to filter traffic from it directly. But most AI platforms don’t yet consistently include referral headers or identifiers, meaning that traffic often appears as "direct" or "organic."

Chris Long, VP of Marketing at Go Fish Digital, explained this challenge: “ChatGPT does tag itself and is making efforts to add more tracking. Occasionally, you’ll see it in GA. But most of the time, LLM referrals show up as homepage traffic. In many instances, someone types your brand directly into their browser after seeing a recommendation in an LLM."

That’s why Eric at Order.co also recommends a low-tech approach: just ask.

"When a prospect conversation happens, ask how they found you. We’ve had multiple buyers say, ‘I asked ChatGPT,’ or ‘I saw you in Perplexity.’ Those mentions matter, and we manually log them when our attribution stack misses it - in Q3, AI will be capturing this and writing it to the CRM right from call transcripts automatically." 

Pro tips:

• Use Peec AI or Profound to understand which prompts your brand is surfacing in, and who else is appearing beside you.
• Monitor homepage traffic spikes as a possible leading indicator of LLM-driven discovery.
• If you're recording calls, search transcripts for mentions of ChatGPT, Perplexity, or “I asked AI.”
• Remember: this is a moving target. Keep revisiting your analytics setup and test new LLM reporting features as they emerge.

This qualitative signal is often your earliest clue that LLMs are driving demand.

Step 2: Start Small. Pick 10–25 Prompts to Target.

Founders often ask: where do I even begin?

The answer is: treat this like a sprint. Pick 10–25 prompts that matter to your ICP. Use Peec AI to see how you rank. Then optimize, or create, content designed to win those queries.

Order.co compiles prompts they use to monitor and compare their performance against competitors within Peec AI.

Peec AItracks and visualizes how specific prompts perform compared to those used by competitors.

Eric shared how Order.co doubled content velocity last quarter and focused that effort on commercial-intent LLM prompts like:

• “Best procurement tools for schools”
• “Finance automation platforms for mid-size teams”

Partnering with Omniscient Digital - they built structured, specific pages that solved for those intents and started tracking rank and conversion monthly.

But what made the difference wasn't just volume, it was use-case specificity.

"I don’t think broad educational content will hold up in LLM results long-term," said Eric. "We used to rank for ‘What is procurement?’ But now we focus on things like ‘What’s the best procurement platform for charter schools?’ because that’s the kind of specific question someone might actually ask an LLM."

I also reached out to another portfolio company to see how they're tackling this challenge. Jonathan Kvarford, Head of GTM Growth at Momentum, shared his systematic research process. He starts by identifying five competitor tools, then feeds all of that data into Claude—competitor analysis, customer pain points from sales calls, and questions people are asking about their space.

"The prompt asks it to identify patterns across all the research and highlight what's unique. Sometimes those unique findings are exactly what we need to double down on." Jonathan added.

This approach allows Jonathan to create a content flywheel that can be repurposed across LinkedIn posts, YouTube videos, TikToks, and multiple blog articles.

And Chris sees similar success patterns across clients: "Mid-funnel, hyper-specific content is what gets picked up. ICP-targetedages like ‘SMS for restaurants’ or ‘CRM for law firms’ convert better from LLM results than generic educational content."

So instead of rewriting your whole blog strategy, create one or two use-case pages per ICP vertical. Then monitor visibility.

Pro tips:

• Consider adding competitor comparison pages (e.g., “YourTool vs. CompetitorTool”)—these are common prompts users type into LLMs.
• Focus on creating intent pages. For example, a Fintech solution might create specific pages or blog articles dedicated to the intent behind what the searcher is looking for vs. the solution itself. 

• "How to accept recurring payments for SaaS startups" > instead of "subscription billing software"
• "Setting up payment processing for nonprofit donations" > instead of "payment gateway"
• "Handling international payments for e-commerce businesses" > instead of "global payment solution"

Step 3: Know Your LLM Source Bias and Platform Strategy

Not all LLMs think alike. Where you show up depends a lot on where those platforms are pulling their information from.

A recent study by Profound, which analyzed over 30 million citations across LLMs, revealed platform-specific differences:

• ChatGPT: ~50% of citations came from Wikipedia, with Reddit also contributing ~11%.
• Google AI Overviews: Draws heavily from community-driven content like Reddit (21%), YouTube (19%), and Quora (14%).
• Perplexity: Strong Reddit dominance (47%), with YouTube a secondary source.

This means your strategy should flex by platform. If you’re optimizing for Perplexity, you’ll want a presence on Reddit. If Google Overviews is your main concern, think YouTube clips, Reddit threads, and Quora answers. For ChatGPT, ensuring you’re cited in Wikipedia or other “canonical” sources can help.

But proceed with caution.

As Chris Long notes, “Reddit is increasingly influential, but it’s not a place you can just drop in with a link and expect to rank. Communities sniff that out quickly.”

We'll get into Reddit in a future piece, but for now: listen before you speak. Look at where your brand is mentioned today, understand the sentiment, and look for authentic participation opportunities.

If you're just starting out, nominate someone internally (many times the founder or early GTM hire) to lead Reddit engagement. This is a long-tail strategy that requires patience.

Their job:

• Spend time understanding Reddit’s norms and subreddits that matter to your buyer.
• Track discussions where your category or competitors show up.
• Participate authentically. Build reputation first. Don’t sell, listen.

Once you’ve earned that presence, Reddit can be an incredible source of feedback. Ask questions. Test ideas. Bring product discussions to life. But only after you’ve built trust.

Also keep in mind: There are regional and bias-based nuances. Reddit’s user base skews U.S.-centric, which may not reflect global buyer patterns. And while LLMs rely on community content today, what data is licensed vs. free will influence what gets prioritized.

Pro tips:

• Use Profound or Peec to identify which domains LLMs are citing in your category.
• Build relationships with influential reviewers or publishers that LLMs commonly pull from (e.g., TechRadar for SaaS).
• Don’t fake your way into Reddit. Monitor threads, understand norms, and earn your presence.

As Chris puts it, “The sites that are influencing LLMs are highly dependent on the industry and audience. For example, let’s say you’re selling child car seats, it might be BabyGearLab.com. You have to understand which sites matter in your space, then find ways to show up there.”

By tailoring your content strategy to each LLM’s sourcing behavior, you’ll dramatically improve your odds of being included in the next buyer query.

Why Wikipedia Still Reigns Supreme

Across both the Profound study and a recent analysis from Ahrefs, one thing was clear: Wikipedia is the most dominant source across LLMs. While Profound analyzed citation patterns across 30M+ prompts, Ahrefs looked at domain-level citation frequency across ChatGPT, Claude, Gemini, and Perplexity. Profound’s analysis of 30M+ citations showed ~50% of ChatGPT’s references came from Wikipedia. Ahrefs similarly found Wikipedia was the top domain cited across ChatGPT, Claude, Gemini, and Perplexity.

So what should founders do about it?

Create a Wikipedia strategy. And think beyond just creating a company listing.

• Create category pages: If you're pioneering a new space, contribute to defining that category on Wikipedia.
• Founders as thought leaders: If the founder is well-published or featured, consider a personal Wikipedia page to increase surface area.
• Cross-link for authority: Wikipedia is a networked knowledge graph. Use relevant citations, credible references, and ensure you're connecting to broader ecosystem pages.

This isn't just about visibility, it's about perceived authority. Wikipedia remains one of the most trusted and broadly cited sources across LLMs, and getting your brand positioned there can drive disproportionate returns.

Pro tips:

• Contribute value-first. Wikipedia has strict editorial standards. Avoid marketing language and ensure all edits are backed by third-party citations.
• If your category doesn’t exist yet, define it. LLMs use these connections to understand context.
• Monitor changes. Because Wikipedia is public, competitors or editors might revise content—set alerts or check regularly.

Getting mentioned here isn't fast or easy, but it's one of the highest-leverage plays you can make for long-term LLM visibility.

Step 4: Shift Your Content Strategy to Align With LLM Behavior

This isn’t just a technical game. How you write, and what you write about, matters more than ever.

The old SEO model prioritized keywords, backlinks, and breadth. But LLMs seem to favor precision (how the data is structured), authority (which site it's coming from), and specificity (how it matches the intent of the searcher).

Let’s look at what’s working today

1. Solution-first headlines beat generic how-tos

Eric emphasized this shift: “We used to rank for ‘What is procurement?’ But now we focus on prompts like ‘What’s the best procurement tool for charter schools?’ That’s what LLM users are actually asking.”

This pivot mirrors the shift toward use-case specificity: not just what your product is, but who it’s for and what it solves.

2. Editorial roadmaps need prompt research, not just keyword research

Platforms like Peec and Profound now offer prompt discovery tools. Treat those as your new editorial lens: start with 10–25 high-intent prompts that match your ICPs. Then build content clusters around those prompts. Think of this as the version of keyword research. 

3. Align content to the platform’s preferred sources

Perplexity loves Reddit. Google favors community responses and YouTube. ChatGPT trusts Wikipedia. Map your editorial strategy accordingly and expect them to change.

• If you’re trying to rank in Google AI Overviews, consider integrating YouTube content or highlighting community answers.
• For Perplexity, think about building Reddit visibility or being cited on domains it favors (you’ll find these using Profound).
• ChatGPT? You may need to secure citations on Wikipedia or other structured sources.

4. Double down on mid-funnel content

Chris sees it across clients: “Mid-funnel, hyper-specific pages outperform top-of-funnel blog posts. Things like ‘CRM for dental practices’ work because they solve a buyer’s exact question.”

Your best content ideas are already in your sales calls. Jonathan at Momentum uses data from closed-won, closed-lost, and active pipeline deals to find the most relevant pain points and trends. “This gives me super rich detail that I’ve never had before,” he shared. Rather than relying on generic AI research or manually reviewing calls, he uses Momentum to surface insights automatically. It captures feedback from hundreds of conversations each month, flags recurring themes, and updates CRM fields with customer pain points, giving him a live pulse on what his ICP cares about. This process allows him to turn emerging questions or objections into new content within days of hearing it from a prospect or customer call. 

Pro tips:

• Refresh old blog content with more definitive answers and use-case targeting.
• Build a monthly content sprint aligned to 3–5 prompts. Track changes in visibility via Peec.
• Collaborate with product marketing to convert feature releases into targeted, prompt-worthy landing pages.

This isn’t about abandoning good writing or SEO principles. It’s about evolving how we package and present that value. LLMs are quickly becoming the front door to your brand.

Step 5: Build for LLM Parsers, Not Just Humans

While traditional SEO rules still matter, LLMs parse and prioritize content differently than search engines. That means your content structure, metadata, and even site architecture need a fresh look.

Chris explains: “Structured content is doubly important for LLMs. Their crawlers aren’t as advanced as Google’s, especially in early-stage platforms like ChatGPT or Perplexity. They need clean, well-organized HTML to interpret and cite your content.”

Here are some tactical adjustments to consider:

1. Use Clear HTML Hierarchies

• Stick to clean heading structures (H1, H2, H3) to help LLMs understand content organization.
• Bullet points, lists, and Q&A formats are easier for parsers to extract.

Jonathan broke this down further, "If you know how to prompt well, those same principles apply to how AI consumes content. LLMs are looking for clear intent matching. They want to connect the intent of the user's prompt to the intent of your content. This means your content structure should follow prompting best practices: H1 with a clear answer, H2 with supporting answers, and a logical hierarchy that helps AI quickly extract relevant information.” 

Jonathan notes that it’s more about whether the intention of this content actually answers this particular question, rather than just splattering SEO keywords throughout an article. 

“Think of your content as a well-structured prompt that helps LLMs deliver fast, accurate answers to users.” Jonathan added.

2. Implement Schema Markup

• Use schema to tag articles, authors, FAQs, and products. This provides context that LLMs can ingest more reliably.

3. Eliminate JavaScript Dependencies

• LLMs like ChatGPT and Perplexity currently can’t render JavaScript-heavy content. Make sure key info is available in raw HTML.

4. Optimize Meta Titles and Descriptions

While LLMs don’t rely exclusively on meta tags, a clear and consistent title structure improves how your content is presented and interpreted.

5. Audit Your Site Architecture

• Ensure your content is easily discoverable through internal linking.
• Create landing pages that group resources by topic, use case, or persona, this helps LLMs contextualize relevance.

Pro tips:

• Ask your web developer to install structured data templates site-wide (especially on your blog and solutions pages.)
• Test your content’s structure with LLMs. Drop a page link into ChatGPT and ask it to summarize, what it extracts will tell you what’s working.
• Consider simplifying overly designed pages. Text-over-image or interactive JS content often fails to get parsed.
• Don’t skip the basics: fast-loading pages, mobile optimization, and internal linking still matter.

Quick Note: Schema Markup for Non-Technical GTM Leaders

If you're writing blog content in HubSpot, using schema markup can help LLMs and search engines better interpret your content. While you can generate schema code manually, adding it post-by-post isn’t scalable.

Here's a more sustainable approach:

• Use a tool like TechnicalSEO’s Schema Markup Generator or ChatGPT to create a sample of the structured data you want (e.g., FAQ, article, etc.).
• Share that sample with a developer.
• Have the developer implement the schema at the template level in HubSpot. This involves editing the core blog template to dynamically insert schema based on fields like title, URL, and author.

Step 6: Measure, Iterate, and Learn as the Landscape Evolves

AI search is not static. Google’s AI Overviews remains in Search Labs, and each platform is rolling out updates rapidly. That means what works today may not work in six months, but that doesn’t mean you should sit still.

Instead, implement a rhythm of experimentation and feedback. This isn’t about rebuilding your strategy constantly, it’s about layering in LLM tactics while staying grounded in SEO best practices.

What to measure:

• LLM visibility: Use Peec or Profound to track prompt inclusion and competitor visibility.
• Attribution: Monitor homepage traffic and direct traffic lifts alongside any content or LLM prompt changes.
• Conversion rates by channel: Create dashboards that compare LLM-sourced leads (where possible) to organic and paid traffic.
• Qualitative indicators: Track mentions of ChatGPT, Perplexity, or “I asked AI” in sales calls and demo requests.

Suggested rhythms:

• Monthly: Review Peec/Profound data. Update content based on changes in prompt inclusion.
• Quarterly: Launch a few new content experiments targeting new prompts or platforms.
• Bi-annually: Revisit your technical setup to ensure it aligns with the latest LLM crawling behaviors.

Pro tips:

• When creating your board updates or GTM reviews, highlight AI search as a standalone channel to reinforce its importance. Show that you’re getting ahead of this new channel.
• We haven’t seen paid search results directly inside LLMs yet, but we expect them to arrive. In the meantime, use changes in your LLM results to inform your paid strategy. If you’re winning a specific prompt or keyword organically through LLMs, you might consider allocating spend on that same keyword or phrase in paid search. 

Eric leaves us with a final reminder, “The rule of search still reigns true - no amount of optimizing will beat creating the things that help searchers get what they need. Solving for the user challenge is still the north star, it just has some new caveats.”

Above all, stay curious.