What a generative engine optimization agency actually does

12 min readJuly 9, 2026By Spawned Team

GEO agencies help brands get cited by ChatGPT, Perplexity, and Gemini. Here's what they do, what it costs, and how to pick one. Real data, no hype.

Marketing professional analyzing AI search visibility reports at a desk

TL;DR: A generative engine optimization (GEO) agency helps your brand show up in AI answers from ChatGPT, Perplexity, Claude, and Gemini instead of just ranking in blue links. They audit how AI models describe you, fix the content and structured data that drives citations, and track your share of AI answers. Full-service work runs $3,000 to $15,000 per month.

What is generative engine optimization, and why does it need its own agency?

Generative engine optimization (GEO) is the work of making your brand the source an AI assistant cites or summarizes when someone asks a relevant question. It splits from traditional SEO on one point: you're not fighting for a spot in a list of ten links. You're trying to become the answer.

The definition most people in the field use comes from a 2023 study out of Princeton, Georgia Tech, and IIT Delhi. The researchers defined GEO as "optimizing content to increase its visibility in generative engine responses" and found that citing authoritative sources, adding statistics, and using fluent language improved content citation rates by up to 40% in their experiments [1].

That 40% comes with caveats. The study ran in a controlled retrieval-augmented generation setup, not live ChatGPT. Real results vary. But the direction holds: AI models favor content that looks authoritative, is grounded in facts, and is structured so a model can lift a clean answer out of it.

Why hire a specialist for this? The skill set is genuinely different from classic SEO. You need people who understand how large language models retrieve and weight information, how retrieval-augmented generation (RAG) pipelines work, how to write content that reads like a quotable source, and how to measure visibility in systems that publish no ranking index. Most SEO agencies don't have that mix yet. The few that do are charging for it.

How does an AI search optimization agency actually work?

A serious generative engine optimization agency runs four phases, though different shops sequence them differently.

First, they audit your current AI visibility. That means running hundreds of queries your target customers plausibly ask across ChatGPT (GPT-4o and o3), Perplexity, Gemini, and Claude, then logging whether your brand shows up mentioned, cited positively, misrepresented, or missing entirely. Tools from our ai seo tools roundup automate parts of this, but good agencies add manual spot-checks, because AI responses are probabilistic and shift with phrasing.

Second, they diagnose why you're invisible or wrong. Common causes: your best content sits behind a login, your product pages carry no structured data, your Wikipedia or Wikidata presence is thin, third-party sources the models trained on never mention you, or your writing chases keyword density instead of factual density.

Third, they execute fixes. This is where the work diverges most from traditional SEO. It includes writing long-form answer-first content, earning mentions in high-authority publications that live in both training data and live web indexes, cleaning up Schema.org markup, keeping your factual record consistent across Crunchbase, LinkedIn, and industry databases, and sometimes filing corrections directly with AI providers.

Fourth, they measure and iterate. AI search visibility metrics and KPIs aren't standardized yet. Agencies mix share-of-voice in answer outputs, citation frequency, citation sentiment, and downstream traffic from AI referrals (visible in GA4 as referrals from chat.openai.com, perplexity.ai, and similar). Some build proprietary dashboards. Others use emerging platforms in the ai visibility tool category.

What does a GEO agency cost?

Pricing is all over the place because the market is less than two years old. Here's an honest range based on published pricing pages and rates commonly quoted by agencies.

| Service tier | Monthly cost (USD) | What you typically get | |---|---|---| | AI visibility audit only | $1,500 to $5,000 (one-time) | Benchmark report, gap analysis, no ongoing work | | Starter retainer | $2,000 to $4,000/mo | Monthly tracking, content recommendations, basic on-page fixes | | Full-service retainer | $5,000 to $15,000/mo | Strategy, content production, PR/link work, structured data, monthly reporting | | Enterprise/custom | $15,000+/mo | Multi-brand, multi-market, custom tooling, dedicated team |

The audit-only engagement is a smart first step if you're not sure GEO is worth the spend yet. It tells you where you stand without committing to a retainer.

A few things push prices up fast. International markets mean query testing in multiple languages. E-commerce catalogs with thousands of SKUs take more work. Regulated industries need legal review before anything publishes. And companies where the AI is actively saying wrong things about them face a harder problem than simple absence, because you have to trace and fix the sources feeding the error.

Nobody has clean published data on average GEO agency pricing yet, since most deals are custom. The figures above come from publicly listed pricing pages and rates discussed in marketing industry forums as of early 2025.

Estimated impact of GEO techniques on AI citation rates

| | | |---|---| | Adding statistics and data | 40% | | Citing authoritative sources within content | 37% | | Fluent, quotation-ready prose | 33% | | Expert quotations in content | 22% | | Formal register and clear structure | 17% |

Source: Aggarwal et al. (Princeton, Georgia Tech, IIT Delhi), arXiv 2023 [1]

What signals does generative AI search actually use to decide who to cite?

This is the question every serious ai search optimization agency is chasing, and honestly, nobody has the full picture. The research that exists points to consistent patterns.

The Princeton/Georgia Tech GEO study found that adding statistics, citing authoritative external sources inside content, and writing fluent, quotation-friendly prose were the highest-impact moves in their test set, lifting citation rates 30 to 40% in aggregate [1]. Adding expert quotations and a more formal register helped too, though less.

Perplexity is more transparent than OpenAI because it's RAG-first: it fetches live web results and synthesizes them. So for Perplexity, classic SEO signals (page authority, indexability, structured data) carry direct weight. For ChatGPT's browsing mode and OpenAI's search features (now folded into ChatGPT), the model blends training data with live retrieval, so your long-term third-party presence matters alongside current web signals [2].

Google's AI Overviews lean hard on Google's existing quality signals: E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness), page speed, and structured data [8]. Google's Search Central documentation describes AI Overviews as built on the same core ranking systems that power regular Search, with extra filters on top [3].

Claude (Anthropic) has no live search product in the same way yet, though Claude with web search is in testing [10]. Its base responses draw on training data, so presence in sources scraped for pretraining (Wikipedia, major news outlets, academic databases) matters for base-model recall.

The practical takeaway: a good strategy targets all four surfaces with slightly different tactics rather than optimizing for one. Our breakdown of ai powered search features compares them surface by surface.

Which generative engine optimization approach is best for AI visibility?

No single approach wins, and anyone selling a silver bullet is selling you something. What works is a combination, weighted by your situation.

If you're a B2B brand with a long sales cycle, the highest-return move is usually publishing deep, factually dense content on the exact questions buyers ask AI assistants during research. BrightEdge's 2024 report found AI Overviews appear for up to 84% of searches in certain query categories, especially informational and comparison queries [4]. That's where your buyers already are. Get there first with content that answers the question directly.

If you're e-commerce, structured data and product feed cleanliness matter more than they do for B2B. Google's AI Overviews pull product details from structured markup and merchant feeds. Perplexity's shopping features do something similar. Clean Schema.org Product markup, accurate pricing, and rich review data all shape whether your products surface.

For brand reputation (making sure AI models say accurate, positive things), the payoff sits in third-party source quality. Models learn what your brand is from Wikipedia, your homepage, major press, and industry databases. A company with a thin or contradictory public record gets hallucinated about. Fixing that is slow, unglamorous work: updating Wikipedia citations, earning accurate press, keeping Crunchbase and LinkedIn current.

Generative AI search optimization for local businesses is still early. Gemini and Google's AI features pull from Google Business Profile data, so local brands should treat GBP hygiene as GEO work. Keep hours accurate, respond to reviews, and make categories and services specific.

How do you evaluate whether a GEO agency is legitimate?

This market has a real credibility problem. GEO became a recognized term in 2023, so plenty of agencies bolted "GEO" onto their site in late 2023 or early 2024 with no track record behind it. Here's what to check.

Ask about their measurement methodology first. A legitimate agency can tell you exactly how they track AI citation share of voice, what tools they use or built, how they sample queries, and how they set a baseline. If they only talk about traffic and traditional SEO metrics, they haven't built a GEO practice.

Ask what they do when an AI model says something false about a client. Good agencies have a process: document the error, find the likely source, correct the source where possible (update Wikipedia, contact the publisher of a wrong article), and petition the AI provider if a formal channel exists. OpenAI has a feedback form. Google offers feedback mechanisms inside AI Overviews and Search Console. An agency that hasn't thought this through hasn't worked with a brand that has a reputation problem in AI.

Ask for anonymized before/after data on citation frequency, more than traffic. AI referral traffic is real and trackable in GA4, but it's not the only signal. ChatGPT can cite you without the user clicking through, and that citation still shapes brand perception. Agencies should track both.

Be skeptical of guarantees. AI outputs are probabilistic. No one can promise ChatGPT will cite you for a specific query. Any agency selling "top citations" or "#1 in AI search" is either lying or doesn't understand how these systems work.

Spawned's AI visibility audit is one way to set a credible baseline before you hire anyone, so you have benchmark data to hold an agency accountable to. See our ai visibility tool page for how the tracking works.

What does a GEO agency deliverable actually look like?

Expect these outputs from a reputable agency, though scope shifts by tier.

An initial AI visibility benchmark report covers which AI surfaces mention your brand, what share of sampled queries in your category include you, what sentiment and factual accuracy look like in those mentions, and which competitors get cited instead. The query sample should run at least a few hundred queries to mean anything.

A content gap analysis maps the questions buyers ask AI assistants against the content you already have. This isn't a keyword list. It's a set of questions the AI gets asked where you're absent or wrong, ranked by business value.

Optimized content itself: some agencies produce it, some spec it and you produce it. The better ones write "answer-first" content, structured so the opening paragraph answers the target question fully, because AI models extract the most quotable text, which usually sits near the top of a well-built page.

Structured data work: Schema.org markup for Organization, FAQPage, HowTo, Product, and Article types depending on your content [5]. This is standard because it's one of the clearest signals to AI retrieval systems about what a page contains [6].

A monthly AI share-of-voice report tracking your citation rate against named competitors across a fixed query set. Without it, you can't measure ROI.

Some agencies also do training-data presence work: getting your brand accurately represented in sources likely to feed future model training, like Wikipedia, industry wikis, and major reference publications. This is long-horizon work with no short-term signal, but it matters for model recall.

How is GEO different from traditional SEO and content marketing?

The differences are real but often oversold. GEO builds on SEO. It doesn't replace it.

Traditional ai seo work improves your spot in a ranked list. GEO work improves how you show up in a synthesized answer. Those goals overlap more than they clash. A page that ranks well in Google is likelier to get retrieved by Perplexity. Content that earns backlinks is content AI models trust more. E-E-A-T signals count for both [8].

What's genuinely new: content structure matters more for GEO. A page that buries its answer in paragraph four serves a language model poorly, because the model lifts the most accessible, quotable text. Query targeting looks different too. Classic SEO chases keywords. GEO chases questions and the intent behind them, because AI assistants respond to full questions, not fragmented keyword strings.

Measurement is the sharpest break. Traditional SEO has 30 years of tooling behind it. GEO measurement is still being invented. There's no Search Console equivalent that shows your citation share across ChatGPT and Perplexity. Agencies and tools are building proxy metrics, but the data infrastructure is immature.

Content marketing has always been about being useful the moment a buyer has a question. GEO makes that goal more literal. If your content answers a question so well that a model lifts it as the answer, you've won that moment, click or no click.

For how google ai search handles AI Overviews and what it means for SEO, we cover the Google-specific angles separately.

What industries benefit most from hiring a GEO agency?

Any industry where buyers open their research with questions rather than branded searches is a strong candidate. That covers most B2B software, professional services, financial products, health information, travel, and higher education.

The most urgent cases are industries where AI answers already dominate the query. BrightEdge's 2024 research found AI Overviews show up most often for health, technology, and finance queries [4]. If you're in one of those and not tracking your AI presence, you're already behind.

Brands with reputation risk in AI outputs, where models say inaccurate things about them, need help regardless of industry. This happens more than people realize. AI models hallucinate pricing, founding dates, product features, leadership names, and regulatory status. A fintech company that AI assistants wrongly describe as "not regulated" has a serious problem that content marketing alone won't fix.

Smaller brands that punch above their weight in third-party coverage, like a regional consulting firm quoted in trade publications for years, often find GEO more tractable than large brands with messy public records. Your existing authority is a GEO asset.

E-commerce at scale usually gets less from a pure GEO agency than from investing in shopping feed quality and product structured data, but the lines blur when consideration runs long. A mattress company gains from GEO on "what mattress is best for back pain" and from clean product markup at the same time.

Can you do generative engine optimization without an agency?

Yes, and many companies should try before hiring. The core practices are learnable.

Start with a self-audit. Ask ChatGPT, Perplexity, Gemini, and Claude 50 questions your buyers actually ask. Note where your brand appears, what it says, and which competitors show up instead. This is free and takes half a day. Our brandrank.ai visibility insights analysis page has more on structuring this kind of competitive benchmark.

Then fix the obvious structural problems yourself. Add FAQ schema to your top pages. Rewrite your "About" page so it answers "what does [brand] do" in the first two sentences. Make sure your Wikipedia entry, if you have one, is accurate and sourced. None of that is agency-only work.

Where in-house teams keep struggling: sustained content production at the factual density GEO needs, earning third-party mentions in publications AI models weight, and measuring results systematically. Those are the activities where an agency earns its money.

Tools in the ai seo tools category help in-house teams handle the tracking and auditing without an agency. The content and PR work is harder to automate.

A sensible path: run the audit yourself, fix the obvious structural issues, then hire an agency (or a GEO-focused freelancer) specifically for content production and third-party mention strategy, where the effort-to-impact ratio is highest.

For teams managing their presence across ai search surfaces, the trick is consistency: the same accurate facts about your brand should appear everywhere a language model might look.

How long does it take to see results from GEO work?

Slower than SEO for training-data changes. Faster than SEO for RAG-based surfaces.

For Perplexity and Google AI Overviews, which pull from live web indexes, well-optimized new content can start showing up in AI responses within days to weeks of indexing. Perplexity cites pages it can fetch and parse. If your page is indexed and answers the query well, it can appear quickly [9].

For ChatGPT's base knowledge (no browsing), changes only propagate when OpenAI updates its training data, on a schedule OpenAI doesn't publish precisely. GPT-4's training data has a knowledge cutoff, though ChatGPT with browsing can reach current content. Corrections to what the base model "knows" about your brand take longest to show up, potentially months, or never until the next model version.

A realistic timeline for a full engagement: four to eight weeks to finish the audit and fix structural issues. Eight to sixteen weeks to see measurable gains in Perplexity and AI Overviews citation rates from content work. Six months or more for gains in base ChatGPT recall from third-party source changes.

That's why agencies promising quick results on every AI surface are oversimplifying. The surfaces work differently, and realistic timelines depend on which ones you're targeting and how much ground you have to make up.

Sources

Frequently Asked Questions

What does GEO stand for in marketing?

GEO stands for generative engine optimization. It's the practice of optimizing content and brand presence so AI assistants like ChatGPT, Perplexity, Gemini, and Claude cite your brand accurately and favorably when answering questions your buyers ask. The term entered common use in 2023, with the first academic definition appearing in a Princeton and Georgia Tech study that year.

Is GEO the same as AEO (answer engine optimization)?

They're close enough that people use the terms interchangeably, but there's a loose distinction. AEO predates generative AI and originally meant optimizing for featured snippets and voice search. GEO is more specific to large language model outputs. In practice, most agencies use both terms to mean the same thing: getting your brand into AI-generated answers rather than just traditional search results.

How do AI models like ChatGPT decide which brands to recommend?

For base knowledge questions, models draw on training data where your brand appeared in authoritative sources. For live search (ChatGPT browsing, Perplexity), they retrieve and synthesize current web pages. Key factors across both: factual density of your content, authority signals from third-party sources, structured data that makes content machine-readable, and consistency of brand information across sources. A Princeton/Georgia Tech study found statistics and external citations improved content citation rates up to 40%.

What's the difference between a GEO agency and an SEO agency that offers GEO?

A real GEO practice needs different measurement tooling, content strategy, and expertise in how language models retrieve information. Many SEO agencies added GEO to their service list in 2024 without changing their methodology. The test is simple: ask how they measure AI citation share of voice across multiple AI surfaces and what they do when a model says something inaccurate about a client. Vague answers point to a rebadged SEO service.

Can a small business afford generative engine optimization services?

Yes, with scope adjustments. A one-time AI visibility audit from a smaller shop runs $1,500 to $3,000 and gives you a prioritized action list to execute in-house. Monthly retainers start around $2,000 at entry-level agencies. Alternatively, in-house teams can handle structural fixes (FAQ schema, answer-first rewrites, Wikipedia accuracy) themselves and hire an agency only for content production and third-party mention work, where specialized help earns its cost.

Does generative engine optimization work for local businesses?

Yes, especially through Google's AI features, which pull from Google Business Profile data. Local brands should treat GBP accuracy as a GEO priority: correct hours, specific service categories, and regular review responses all feed how Google's AI surfaces represent local businesses. For non-Google AI surfaces, local businesses have less direct control but benefit from consistent NAP (name, address, phone) data across directories, which AI models aggregate.

How do you measure the ROI of GEO?

The most direct measure is AI referral traffic, visible in GA4 as sessions from chat.openai.com, perplexity.ai, gemini.google.com, and similar. Beyond that, good agencies track citation frequency across a fixed query set (how often your brand appears in AI answers to target questions) and citation sentiment. Connecting citation gains to pipeline means tagging AI-referred visitors the way you'd tag any UTM campaign, then following their conversion path.

What content formats work best for AI citations?

Content that answers a specific question in the first paragraph, includes verifiable statistics with named sources, uses clear subheadings that match how people phrase questions, and avoids dense jargon performs best in AI retrieval. FAQ schema is high-value because it structures Q-and-A content in a machine-readable format models can extract directly. Long-form, factually dense pages consistently beat thin pages in the research that exists.

Which AI search engine should I prioritize for GEO?

Prioritize where your audience actually is. For most B2B brands, ChatGPT has the largest user base (over 300 million weekly active users as of early 2025 per OpenAI's own reporting) and high-intent research traffic. Perplexity is growing fast among tech-forward buyers and is more transparent about its retrieval. Google AI Overviews reach the broadest general audience. A query audit across all three tells you where you're already present and where the gap is biggest.

Can I get my brand cited in ChatGPT without a third-party agency?

Yes. The core tactics: publish factually dense answer-first content on your site, earn accurate mentions in high-authority publications (news sites, industry outlets, Wikipedia), add structured data markup, and keep your brand's public factual record consistent. An agency speeds this up but doesn't monopolize it. In-house teams with strong content and PR chops can do meaningful GEO work, especially for live-retrieval surfaces like Perplexity.

What is retrieval-augmented generation and why does it matter for GEO?

Retrieval-augmented generation (RAG) is the architecture where an AI model fetches external documents in real time before generating an answer, rather than relying on training data alone. Perplexity is built almost entirely on RAG. ChatGPT with browsing uses it. Google AI Overviews use a RAG-like system over the Google index. For GEO, RAG-based systems respond faster to current web optimization than base-model systems, so your content improvements show up sooner.

What structured data schema types matter most for GEO?

FAQPage schema is the most directly useful because it structures question-and-answer content that AI models extract easily. Article schema (with author, date, and publisher fields) helps establish content credibility. Organization schema keeps brand identity accurate. Product schema matters for e-commerce. HowTo schema helps for instructional content. All are documented at Schema.org and free to implement. Google's Rich Results Test verifies your implementation.

How do I know if an AI model is saying something wrong about my brand?

Run a systematic query audit: ask ChatGPT, Perplexity, Gemini, and Claude direct questions about your brand, products, pricing, leadership, and industry position. Note any factual errors. Common hallucinations include wrong founding dates, incorrect pricing, inaccurate product descriptions, and wrong regulatory status. Once you spot errors, trace them to the likely source and fix there first, before filing feedback with AI providers directly.

generative engine optimization agency

Ready to try it?

Build your first app in a few minutes.

Start Building