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 is no longer the whole story — and for AI startups selling to technical or sophisticated buyers, the gap between that playbook and what is actually driving discovery is widening fast.
Today, buyers are skipping Google altogether. They are asking ChatGPT or Claude for software recommendations. They are relying on Perplexity to summarize competitive landscapes. They are getting product suggestions directly from Google's AI Overviews before they ever click through to an organic result.
And here is what might surprise most founders: that traffic converts better. Significantly better. One growth leader at a B2B procurement startup we work with described seeing LLM-referred traffic convert at 30% versus 5% for traditional SEO. "It started as paranoia," he told us. "We were just trying to make sure we weren't losing traffic. But now it's a core part of our strategy."
Across our portfolio at Milestone AI Ventures, we are hearing similar stories. LLMs are emerging as a new top-of-funnel driver — and the founders who treat this as a structural channel shift rather than a curiosity are getting ahead of it. Here is the practical guide we share with our portfolio companies on how to break through the noise and build a presence in AI search.
Before You Begin: Focus Matters More Than Completeness
If you are a small team — and most of the companies we back are — you cannot afford to overhaul your entire content strategy every quarter based on the latest platform update. The SEO fundamentals still apply:
- Write for humans first.
- Maintain clean site structure and fast page load times.
- Focus on helpful, intent-driven content that actually answers the questions your buyers are asking.
Treat LLMs as a new distribution channel to layer on top of existing fundamentals, not a reason to start over. Start small: pick a handful of LLM prompts that are genuinely relevant to your buyer's decision process, see how you show up today, and build from there. Google's AI Overviews are already rolling out broadly — this shift is not hypothetical. But the right response is disciplined experimentation, not panic-driven strategy pivots.
This new discipline has spawned a term worth knowing: Generative Engine Optimization (GEO). It refers to content strategies designed specifically for how LLMs surface answers — not just how search engines rank pages. The underlying principles are different enough from traditional SEO that treating them as identical is a mistake.
Step 1: Understand Where You Stand Today
The first step is diagnostic. Before you can optimize, you need to understand how and where you are already appearing (or not appearing) in LLM results.
A new category of tools has emerged to help with exactly this. Peec AI and Profound are two we recommend to portfolio companies — they track how your site is referenced across AI systems including ChatGPT, Perplexity, and Google's AI Overviews. Think of Peec AI as SEMrush for LLMs: it lets you monitor prompt visibility, benchmark against competitors, and identify what content is breaking through into AI-generated responses.
Connecting LLM visibility to the traffic you actually see in your analytics requires some creative interpretation. ChatGPT does tag its referrals in some cases, allowing you to filter for it in Google Analytics. But most AI platforms do not consistently include referral headers, meaning that traffic often appears as "direct" or gets attributed to organic search. Homepage traffic spikes that correlate with content publishing are often a leading indicator of LLM-driven discovery.
The most reliable attribution signal for many companies, counterintuitively, is qualitative: just ask. When a prospect conversation happens, ask how they found you. "I asked ChatGPT" and "I saw you in Perplexity" are increasingly common answers that attribution stacks miss entirely. Logging these mentions manually — until your CRM integrations can capture them automatically from call transcripts — gives you a real-time pulse on whether LLMs are driving demand.
Practical starting points:
- Use Peec AI or Profound to understand which prompts your brand is surfacing in, and who else appears alongside you.
- Monitor homepage direct traffic as a potential leading indicator of LLM-driven discovery.
- Search sales call transcripts for mentions of ChatGPT, Perplexity, or variations of "I asked AI."
- Revisit your analytics setup quarterly — this is a fast-moving area and new reporting features are appearing regularly.
Step 2: Pick 10–25 Prompts to Target
When founders ask us where to start, our answer is always: treat this like a focused sprint, not an open-ended strategy initiative. Pick 10–25 prompts that genuinely matter to your ideal customer profile. Use a tool like Peec to see how you currently rank. Then optimize existing content or create new content specifically designed to win those queries.
The content strategy that works in LLM results is not the broad educational content that dominated early SEO — "What is procurement?" or "What is MLOps?" Those queries, which once drove reliable top-of-funnel traffic, are increasingly answered by LLMs directly from Wikipedia or other canonical sources, with no click-through to your site. What works instead is use-case specificity: "What is the best procurement platform for charter schools?" or "How do I automate ML experiment tracking for a distributed team?" These are the kinds of specific questions that buyers actually ask LLMs when they are in evaluation mode.
One approach we have seen work well in portfolio companies is competitive intelligence-driven content creation: identify your five most relevant competitors, combine competitor analysis with customer pain points from your own sales calls and the questions people are asking about your category, and use that research to identify content gaps where you can be the most definitive answer available. This produces a content flywheel that can be repurposed across multiple formats and channels.
Mid-funnel, hyper-specific content consistently outperforms top-of-funnel educational content in LLM results. ICP-targeted pages — "AI monitoring for financial services compliance teams" or "ML pipeline orchestration for healthcare data" — convert better from LLM results than generic category overviews. The buyer who asks a specific question and receives a specific answer is further along in their decision process than the one starting with a definitional query.
Content formats worth prioritizing:
- Competitor comparison pages (e.g., "YourProduct vs. Competitor") — buyers frequently run these queries in LLMs when narrowing their evaluation list.
- Use-case specific landing pages, one or two per ICP vertical, with concrete outcomes and credible evidence.
- Intent-focused pages that address the problem the buyer is trying to solve, not just the product category you represent.
Step 3: Know the Platform Differences — They Are Significant
Not all LLMs pull from the same sources. Understanding where each platform derives its information shapes which visibility-building activities are highest-leverage for your situation.
Profound analyzed over 30 million citations across major LLMs and found meaningful platform-specific differences:
- ChatGPT: Approximately 50% of citations came from Wikipedia, with Reddit contributing roughly 11%.
- Google AI Overviews: Draws heavily from community-driven content — Reddit (21%), YouTube (19%), and Quora (14%).
- Perplexity: Strong Reddit dominance (47%), with YouTube as a meaningful secondary source.
The practical implication is that platform-specific strategies yield better results than trying to optimize for "LLMs" as a monolithic category. If Perplexity is where your buyers are discovering alternatives, Reddit presence matters significantly. If Google AI Overviews is the primary surface, YouTube content and community answer platforms are higher-leverage than pure blog SEO. If ChatGPT visibility is the goal, canonical sources — Wikipedia, high-authority publications, structured data — are what matter.
A word of caution on Reddit specifically: it is enormously influential and increasingly cited across multiple LLM platforms, but it is not a channel you can enter with marketing intent and expect results. Communities detect promotional content quickly and react negatively. The right approach is to nominate someone internally — often a founder or early GTM hire — to build genuine participation over time. Start by understanding the norms of subreddits relevant to your buyers. Track discussions where your category appears. Build reputation through authentic contribution before attempting any kind of brand positioning. This is a long-tail strategy that requires patience, but the brands that have done it well have built disproportionately strong LLM visibility.
Why Wikipedia Deserves a Dedicated Strategy
Across every study of LLM citation patterns — including both Profound's 30M+ citation analysis and Ahrefs' domain-level citation frequency analysis across ChatGPT, Claude, Gemini, and Perplexity — Wikipedia emerges as the single most dominant source. This is not a coincidence. LLMs were trained on Wikipedia extensively, trust it as a canonical reference, and continue to draw heavily on it in response generation.
For AI startups, this means that a Wikipedia strategy deserves dedicated attention — and thinking beyond just a company page:
- Category definition: If your company is pioneering a new category or subcategory of AI software, contributing to how Wikipedia defines that category gives you authority over how LLMs describe the space you operate in.
- Founder visibility: If your founders have meaningful published work, speaking history, or media coverage, a personal Wikipedia page with accurate third-party citations increases your surface area in LLM results for queries about thought leadership in your space.
- Cross-linking: Wikipedia's link structure influences how LLMs understand relationships between concepts. Being cited on relevant ecosystem pages — programming languages, platforms, frameworks — positions your company within the knowledge graph that LLMs use to contextualize responses.
Wikipedia has strict editorial standards. Marketing language fails immediately. All edits must be backed by third-party citations from reliable sources. The investment required to build a legitimate Wikipedia presence is real — but the leverage from being positioned there is among the highest available in LLM optimization.
Step 4: Restructure Your Content Strategy Around LLM Behavior
The old SEO model prioritized breadth, keyword density, and backlink accumulation. LLMs appear to favor precision in how data is structured, authority of the citing domain, and specificity in matching the intent behind the query. This shift has meaningful implications for how AI startup content teams should spend their time.
Solution-first headlines beat generic how-tos
Shift from "What is [category]?" to "What is the best [specific solution] for [specific buyer type]?" The former gets handled by general knowledge sources. The latter is where your content has genuine differentiation potential.
Build editorial roadmaps around prompt research, not just keyword research
Platforms like Peec and Profound now offer prompt discovery features. Use them as your editorial planning tool: start with 10–25 high-intent prompts that represent real queries from your ICP, then build content clusters designed to be the most thorough and credible answer to those specific prompts. This is a fundamentally different planning approach from keyword-volume-driven SEO calendars.
Align content to each platform's preferred sources
Map your content format strategy to where your target buyers are discovering alternatives. YouTube content and Reddit engagement for Perplexity; structured long-form and Wikipedia citations for ChatGPT; community-answer formats for Google AI Overviews. These are not mutually exclusive — but prioritization matters for resource-constrained teams.
Double down on mid-funnel specificity
The highest-value content in LLM optimization is mid-funnel: specific enough to match an evaluation-stage query, authoritative enough to be cited as a definitive reference. Your sales calls and closed/lost data are the best source for this content — they contain the real questions buyers are asking when they are actively evaluating solutions. Founders who build a process to turn recurring sales-call themes into content within days of hearing them from prospects have a structural content advantage that compounds over time.
Step 5: Build Your Site for LLM Parsers, Not Just Human Readers
LLMs parse and prioritize content differently from search engine crawlers — and differently from human readers. Several technical adjustments significantly improve how reliably your content gets extracted, understood, and cited.
Clean HTML hierarchies: Use heading structures (H1, H2, H3) consistently. Think of your content architecture as a prompt: H1 with a clear answer to the query, H2 with supporting evidence, logical hierarchy that allows an AI to extract relevant information quickly. Bullet points, lists, and Q&A formats are easier for LLM parsers to extract than dense paragraphs.
Schema markup: Implement schema to tag articles, authors, FAQs, and products. This provides structured context that LLMs can ingest more reliably than unstructured HTML. If you are using a CMS like HubSpot, work with your developer to implement schema at the template level rather than on a post-by-post basis — the latter does not scale.
Eliminate JavaScript dependencies for key content: Current LLM crawlers, particularly those used by ChatGPT and Perplexity, cannot reliably render JavaScript-heavy content. Key product information, pricing context, and value propositions should be available in clean HTML that parses without JavaScript execution.
Test your content with LLMs directly: Drop a page link into ChatGPT and ask it to summarize the page. What it extracts will tell you immediately what is and is not working in your content structure. This is the fastest feedback loop available and costs nothing.
Simplify overly designed pages: Interactive JavaScript components, text-over-image layouts, and animation-heavy designs often fail to get parsed at all by LLM crawlers. The pages that get cited most reliably in LLM results tend to be clean, well-structured, and text-forward.
Step 6: Measure, Iterate, and Build This Into Your Rhythm
AI search is not a static channel. Google's AI Overviews continue to evolve; Perplexity is rolling out new features regularly; the citation patterns we described above will shift as these platforms develop. The right response is not to rebuild your strategy with every update — it is to build a lightweight, regular measurement rhythm that allows you to adapt.
What to measure monthly:
- LLM visibility for your 10–25 target prompts via Peec or Profound
- Homepage and direct traffic trends as a leading indicator of LLM-driven discovery
- Qualitative mentions of AI platforms in sales calls and demo requests
- Conversion rates from any identifiable LLM-sourced traffic
What to do quarterly:
- Launch two to three new content experiments targeting high-priority prompts or new platform surfaces
- Revisit your technical setup for LLM parsability — new crawling behaviors emerge regularly
- Expand your target prompt list as your product expands into new use cases
One practical suggestion: include AI search as a standalone channel in your board updates and GTM reviews. Reporting on it explicitly signals that you are tracking an emerging shift that most competitors are ignoring — and it creates accountability for the experimentation rhythm.
The underlying principle of good search has not changed. No amount of optimization beats creating content that genuinely helps buyers get what they need. Solving for the buyer's actual challenge remains the north star — it just has new caveats about how that value gets surfaced and by whom. LLMs are rapidly becoming the primary front door through which buyers discover solutions in B2B markets. The AI startups that build a systematic presence in that channel now will have a distribution advantage that is difficult to replicate once the channel matures.
Priya Nair is a Principal at Milestone AI Ventures, where she leads GTM strategy work with portfolio companies and covers AI-native SaaS investments. She previously held product marketing roles at developer-focused software companies. The views expressed here are her own and do not constitute investment advice.