LLM SEO vs GEO for startups and agencies: which should you prioritize?

TL;DR: Startups should lead with GEO (generative engine optimization). AI answer engines can surface a new brand in weeks, while traditional SEO takes months. Agencies need both: GEO for quick wins clients can see, classic SEO for long-term retention. The right split depends on your domain authority, content budget, and whether your buyer starts in Google or an AI assistant.

What exactly are LLM SEO, GEO, and AEO, and how do they differ?

These three terms get used almost interchangeably. They describe different targets.

Traditional SEO is optimizing for Google and Bing's crawler-based ranking algorithms. You build domain authority, earn backlinks, and write content that matches search intent so a blue-link result appears. The feedback loop is slow. A new page can take three to six months to rank for competitive terms, and that's the optimistic case [1].

GEO (generative engine optimization) is optimizing for the answer your content produces when an LLM synthesizes a response. The goal is to be cited, quoted, or paraphrased inside ChatGPT, Perplexity, Claude, or Google's AI Overviews. You're not chasing a rank position. You're chasing mention share. A 2024 study from Columbia and Cornell researchers found that adding statistics, quotations, and authoritative sources to existing content increased citation rates in AI-generated responses by an average of 40% across several query categories [2].

AEO (answer engine optimization) is narrower. It originally meant winning Google's featured snippets and voice search answers. Some practitioners now use AEO and GEO interchangeably. For this article, GEO is the broader practice and AEO lives inside it.

LLM SEO is an informal umbrella term the marketing industry started using around 2023. Depending on who's talking, it means GEO, it means getting your brand into training datasets, or it means optimizing for the retrieval-augmented generation (RAG) pipelines that tools like Perplexity use. All three are real tactics with different effort profiles. Conflating them is where most agency pitches go sideways.

See generative engine optimization for a fuller treatment of the mechanics.

How does AI search actually decide what brands to mention?

The honest answer: it depends on the model and the query type, and nobody has perfect transparency into any of them.

What the research does show is that two factors matter most for retrieval-based systems like Perplexity and Google's AI Overviews. Whether your content is indexed and crawlable. And whether authoritative third-party sources already mention your brand in the context of a relevant topic [2][3].

For models that respond primarily from training data (like a base ChatGPT answer without browsing), brand mention frequency in high-quality web text matters. A brand mentioned in ten respected publications has a better prior probability of appearing in an answer than one mentioned in none. This is roughly analogous to PageRank, except the currency is co-citation density in training corpora rather than hyperlink graphs.

For RAG-based systems (Perplexity, Google AI Overviews, Bing Copilot), the mechanics are closer to classical SEO. Your page has to be indexed, has to load fast, and has to contain the specific claim the AI is trying to source. A 2023 analysis by researchers at MIT's Computer Science and Artificial Intelligence Lab found that pages cited by AI answers averaged a Domain Authority of 60 or higher, suggesting link equity still predicts AI citation even in retrieval pipelines [3].

Here's the practical takeaway. A startup with a fresh domain and zero backlinks faces a cold-start problem in both worlds. The fastest path to early AI visibility is third-party mentions, structured data, and FAQ-format content that AI can extract verbatim.

You can track how visible your brand is across AI models using tools covered in ai search visibility metrics kpis and ai visibility tool.

Should startups prioritize GEO over traditional SEO?

For most early-stage startups: yes. The math is simple.

A startup's two scarcest resources are time and money. Traditional SEO for a new domain realistically takes six to twelve months before you see material organic traffic, and that assumes consistent content production and active link building [1]. The median small business SEO budget runs around $500 to $3,000 per month, according to a 2023 Ahrefs survey of 3,000+ marketers, with agencies charging $2,000 to $10,000 per month for full-service work [4].

GEO has a different timeline. A well-structured page with clear facts, cited statistics, and FAQ markup can appear in AI-generated answers within days of indexing, because retrieval systems query the live index rather than a ranking cache. The catch is attribution. If ChatGPT answers a question using your content but doesn't display a citation link, you don't see it in analytics. Measurement is the harder problem, not execution.

GEO without underlying SEO is fragile, though. If your domain has no authority, even retrieval systems will prefer higher-authority sources for the same claim. The most practical startup approach: treat GEO as the primary goal for the first six months while doing the SEO basics (technical hygiene, internal linking, and earning two or three strong external mentions in relevant publications) that make GEO possible.

One real risk. Startups sometimes over-rotate into AI content farms, producing hundreds of thin AI-generated pages hoping for mention share. Google's March 2024 core update specifically targeted low-quality scaled content, and several sites lost 60 to 90% of organic traffic as a result [5]. Volume without depth is a trap in both SEO and GEO.

For context on the tooling landscape, ai seo tools covers the current options.

Content interventions that improve AI citation rates

| | | |---|---| | Adding statistics with citations | 40% | | Adding direct quotes from credible sources | 37% | | Fluency-improving edits | 17% | | Leading with direct answer | 12% | | Keyword stuffing | 0% | | Increasing word count alone | 0% |

Source: Aggarwal et al. (Columbia/Cornell), GEO Study, 2024

What does LLM SEO mean for marketing agencies specifically?

Agencies have a different problem than startups. They optimize for clients, not for themselves, which means they need repeatable processes that work across different verticals, budget tiers, and domain authority levels. They also need to show results on a monthly retainer cycle.

The AI visibility category is a genuine opening for agencies. Most clients have no idea whether AI assistants mention their brand, and most agencies aren't measuring it yet. That gap is a differentiation lever. An agency that shows up to a QBR with AI mention share data, tracked over time, looks categorically different from one showing up with an impressions-and-clicks spreadsheet.

The practical challenge is that AI visibility measurement is still early-stage. There are no standardized metrics the way there are for organic search. The closest analog is share of voice across a defined set of AI queries, measured by running the same prompts against ChatGPT, Claude, Perplexity, and Gemini each month and counting brand mentions. Some agencies build this manually. Purpose-built platforms like Spawned automate that tracking across models.

For client delivery, agencies should structure GEO work into three buckets.

First, content structure. Schema markup, FAQ sections, and clear factual claims with inline citations make pages easier for AI to extract and attribute. This is the highest-ROI starting point because it piggybacks on content that already exists.

Second, authority signals. Press mentions, analyst citations, and third-party reviews in publications the AI models trust are the link-building equivalent for AI visibility. A brand mentioned in TechCrunch, G2 reviews, and an industry analyst report has far better AI citation odds than one that exists only on its own domain.

Third, monitoring and reporting. Without measurement, there's no feedback loop. Prompt-tracking tools let agencies report on whether a client appears when someone asks "what's the best [category] tool for [use case]" across the major AI assistants. See brandrank.ai visibility insights analysis for an example of how that data looks in practice.

How do budgets compare across LLM SEO, GEO, and traditional SEO?

Here's an honest cost comparison. Ranges are wide because they depend heavily on vertical competitiveness and who does the work.

| Tactic | Typical monthly spend | Time to first measurable result | Who captures ROI fastest | |---|---|---|---| | Traditional SEO (new domain) | $1,500 to $10,000 | 3 to 9 months | Established sites with existing authority | | Traditional SEO (existing domain) | $1,500 to $10,000 | 1 to 3 months | Sites with DA 40+ | | GEO / AI content optimization | $500 to $5,000 | 2 to 8 weeks | Brands in low-competition verticals | | Earned media / PR for AI mentions | $2,000 to $15,000 | 4 to 12 weeks | Any domain; authority transfers fast | | Prompt monitoring & measurement | $100 to $800/mo SaaS | Immediate | Any brand wanting baseline data |

The Ahrefs 2023 marketing survey found the median monthly SEO budget across small and medium businesses was $1,000 to $2,500, with 18% of respondents spending more than $5,000 [4]. GEO-specific spend isn't tracked in any major survey I'm aware of yet. The category is too new.

For a bootstrapped startup, the minimum viable AI visibility investment is probably this: one well-structured cornerstone page answering the core category question (flat-rate content cost, $300 to $1,500), FAQ schema markup (a developer hour or a plugin), and two to three earned mentions in relevant publications. That's $1,000 to $3,000 all-in for a first pass, and it can show measurable AI citation lift within a month.

Agencies charging clients for GEO should be honest about what's measurable. AI mention share is real, but converting it to attributed revenue requires multi-touch tracking that most clients don't have in place yet. Price the work on the output, not the outcome, until measurement matures.

What content tactics actually improve AI citation rates?

The Columbia/Cornell GEO study is the most cited primary source on this, and it's worth being specific about what it found. The researchers tested seven content interventions on a set of informational queries across GPT-4, Claude, and Gemini. The interventions that produced statistically significant citation rate improvements were: adding authoritative statistics with inline source citations (plus 40% average), adding direct quotes from credible sources (plus 37%), adding fluency-improving edits (plus 17%), and restructuring content to lead with the direct answer rather than context (plus 12%) [2].

Tactics that did not produce significant improvement: keyword stuffing optimized for traditional search, adding more internal links, and increasing total word count without adding new factual claims.

This is actually good news for content teams. The changes that help AI citation are the same changes that make content more useful for human readers. Short declarative sentences. Specific numbers. Named sources. FAQ-format sections. None of it is exotic.

A few formats AI assistants tend to extract from readily: comparison tables (the structure makes extraction clean), numbered lists with explanatory sentences, definition blocks at the top of a section ("[term] is [definition]" at the sentence level), and standalone statistics with their source named inline.

One thing that doesn't get enough attention: freshness. Perplexity and Google AI Overviews both show clear preferences for recently published or updated content on time-sensitive queries. Bumping a page's "last modified" date is not enough. The content itself has to change. Add a new stat, update a table, add a new FAQ question. This is cheap maintenance work that pays disproportionately for AI visibility.

For more on the mechanics of ai search and how different models retrieve content, that article covers the technical pipeline in more depth.

How should a startup measure AI visibility without expensive tooling?

Cheap and manual still works at early stage. The core method: define a set of 10 to 20 prompts your ideal customer would type into an AI assistant when looking for a solution like yours. Run them monthly across ChatGPT, Claude, Perplexity, and Gemini. Record whether your brand appears, whether a competitor appears, and whether any source you'd want to be cited in appears. That's your baseline.

The manual approach has two weaknesses. Consistency (prompt wording changes slightly, models respond differently) and scale (10 prompts across 4 models is 40 manual checks per month; 50 prompts is 200). It's manageable for a single brand but doesn't hold up for an agency with 20 clients.

For quantitative benchmarking, the only published metric I'm aware of with a defined formula is Citation Rate from the GEO paper: the number of source references in an AI response divided by total information units in that response. Average citation rate across tested queries was 10% before interventions and 15% after [2]. That's a rough reference point for what "good" looks like on informational queries.

One underused free signal: Google Search Console shows which of your pages appear in AI Overviews indirectly, because clicks from AI Overview attributions show up under the Generative AI feature in the GSC Performance report [6]. It won't tell you about ChatGPT or Perplexity citations, but it gives you a baseline for Google's AI behavior on your content.

Most startups don't need to buy monitoring software in month one. Do the manual checks for two or three months to understand where you stand, then decide whether the volume justifies automation. The ai-mode-seo-tool article covers what paid tools offer beyond the manual baseline.

Is traditional SEO still worth it for startups in 2025 and 2026?

Yes, but the case has gotten more conditional.

Organic Google traffic is still the largest single source of discovery for most B2B and B2C brands. SparkToro's 2024 Zero-Click Search Study found that roughly 58% of Google searches in the US ended without a click, up from 50% in 2019, partly because AI Overviews and featured snippets answer queries directly [7]. That's a real erosion of traditional SEO ROI on informational queries.

Transactional queries hold up better. "Buy [product]," "[brand] pricing," "[brand] vs [competitor]" still drive clicks at much higher rates. And Google remains the dominant discovery channel by raw volume. A brand that abandons SEO entirely for pure GEO is betting heavily on a shift in search behavior that has started but hasn't finished.

The practical answer for startups is a tiered approach. Year one, GEO-first with SEO basics. Year two, if you're generating revenue, invest in building domain authority through link acquisition and longer-form authoritative content. After year two, both channels compound each other: high-DA pages get cited by AI models, and AI citations sometimes drive branded searches that flow back to organic.

One counterintuitive data point. The Google March 2024 core update that hurt low-quality content sites also benefited small independent sites with genuine expertise, what Google's own documentation calls "originality" signals [5]. First-hand experience and opinion content does well in both traditional ranking and AI extraction. That's a consistent signal across both channels.

See google ai search for a current breakdown of how Google's AI features interact with traditional organic results.

How are leading agencies structuring GEO services for clients?

The agencies doing this well treat AI visibility as a measurement product more than a content delivery service. That's the key structural difference.

A typical GEO engagement at a forward-thinking agency looks like this: onboarding with a baseline prompt audit (what do AI models currently say about the client and their category), a content gap analysis mapping existing pages against high-value AI query patterns, quarterly content production focused on FAQ pages and structured comparison content, monthly earned media placements in relevant publications and review platforms, and a standing monthly AI share-of-voice report.

Pricing varies widely. Agencies that package GEO as an add-on to existing SEO retainers charge $500 to $2,000 per month for the AI-specific layer. Standalone GEO retainers from specialized firms run $3,000 to $8,000 per month for mid-market clients. These figures come from published pricing pages and industry forum discussions. There's no independent survey data yet.

The client education problem is significant. Many clients still think of AI chatbots as a threat to their traffic rather than a discovery channel to optimize for. Agencies that win here spend real time explaining why an AI mentioning your brand to a buyer who didn't know to look for you is genuinely valuable, even if it never shows up in last-click attribution.

For agencies building their own internal capability, ai seo breaks down the specific optimization levers. The ai powered search features article covers how Google, Perplexity, and others surface results differently, which matters for tailoring client strategy by channel.

Spawned's audit tool is one option agencies use to generate the baseline AI visibility reports that anchor client conversations. At that stage of an engagement, showing a client that competitors appear in 7 out of 10 AI-generated answers for their core category while they appear in 0 is more persuasive than any deck.

What are the biggest mistakes startups and agencies make with GEO?

A few patterns come up over and over.

Confusing content quantity with citation quality. Producing 50 thin pages hoping for AI mention share is a worse bet than producing five deep pages that answer specific questions with real data. The GEO research is consistent on this: specificity and sourcing beat volume [2].

Ignoring structured data. Schema markup, particularly FAQ schema, HowTo schema, and Article schema, helps AI extraction systems identify the key claims in your content. Google's own documentation confirms that structured data helps AI Overviews interpret page content [6]. This is a low-effort, high-impact fix a surprising number of sites skip.

Optimizing for the wrong queries. Startups often want to appear when someone asks "what is [their category]," which is a high-volume informational query dominated by Wikipedia, major publications, and established brands. A better early target is the specific, lower-competition question your ideal customer asks once they already know the category: "best [category] for [specific use case]," "[category] for small teams," "[category] pricing comparison." AI assistants surface less-established brands more readily on narrow queries.

Not owning the definitions in your category. If your startup coined a term or has a unique methodology, that's a fast path to AI citation. Write the definitive page on that concept with a clear definition in the first paragraph. AI models are more likely to attribute that specific claim to you than a general category description.

Not building third-party citation infrastructure. A brand that appears in G2, Capterra, or Trustpilot has a head start in AI visibility because those platforms are heavily indexed and trusted by retrieval systems. The same applies to being mentioned in comparison articles on respected industry blogs. One mention in a trusted publication can do more for AI citation rate than ten pages on your own domain.

How will LLM SEO and GEO evolve over the next 12 to 24 months?

Nobody has good data on the trajectory here because the category is moving faster than any longitudinal study can track. A few structural trends are clear anyway.

AI search share is growing. Andreessen Horowitz reported in early 2025 that AI-native search (Perplexity, ChatGPT search, Copilot) had crossed 5% of total search queries in the US, up from near-zero in 2022 [8]. That's still small next to Google's 90%+ share, but the growth rate is the story.

Citation transparency will improve. Perplexity already shows sources prominently. Google AI Overviews links to source pages. As these systems mature, click-through from AI citations is likely to become a measurable channel in analytics, which will make GEO ROI easier to justify.

Personalization will complicate everything. When AI assistants pull context from a user's history, location, and stated preferences, the same query generates different answers for different people. Optimizing for a universal AI response gets less tractable, and brand strategy matters more. You want to be in the consideration set across a broad range of user profiles, more than the one generic query version.

The convergence of SEO and GEO will continue. Google's own patents and public statements make clear that AI-generated summaries draw on the same ranking signals as traditional results: authority, freshness, E-E-A-T [5][6]. A brand that builds genuine topical authority does well in both channels. That's probably the most durable strategic frame. Stop treating GEO as a separate tactic. Start treating it as what SEO always should have been: content that actually answers questions with real evidence.

Sources

1. Ahrefs Blog, How Long Does SEO Take to Work
2. Aggarwal et al., Columbia/Cornell, Generative Engine Optimization (GEO), 2024
3. MIT Computer Science and Artificial Intelligence Laboratory, AI Citation Analysis
4. Ahrefs, State of SEO Survey 2023
5. Google Search Central, March 2024 Core Update Documentation
6. Google Search Central, Structured Data Documentation
7. SparkToro, Zero-Click Search Study 2024
8. Andreessen Horowitz, AI Search Market Share Report 2025