AI SEO agency: what they do, what they cost, and how to pick one

TL;DR: An AI SEO agency helps your brand appear in answers from ChatGPT, Gemini, Perplexity, and Google's AI Overviews, more than in blue-link results. The work is structured content, entity authority, citation building, and technical schema. Retainers run roughly $3,000 to $15,000 a month depending on scope. The field is new enough that most agencies are still figuring out what actually works.

What does an AI SEO agency actually do?

Traditional SEO is about ranking on page one of Google. An AI SEO agency is chasing something narrower and stranger: getting a language model to mention your brand by name when a user asks a relevant question. Different problem entirely.

The work falls into a few buckets. First, content restructuring. AI models are trained on, and retrieve from, text that is clear, attributed, and structured. Agencies rewrite or add to your content so a sentence like "the average cost of X is $Y, according to Z" is explicitly present and easy to quote. Second, entity building. If ChatGPT doesn't "know" your brand as a real, distinct thing, it won't cite you. Agencies build that recognition through structured data, consistent NAP (name, address, phone) signals, and mentions on authoritative third-party sites. Third, schema and technical markup. FAQ, HowTo, Product, and Review schema all help AI crawlers pull clean answers. Fourth, citation and PR work. A brand mentioned in TechCrunch, G2, and a few .edu or .gov adjacent pages has a much better shot at showing up in model outputs than one whose only content lives on its own domain.

Some agencies also monitor AI search visibility, tracking how often a brand appears in responses across ChatGPT, Perplexity, Claude, and Gemini. The tools for this are early, but they exist. AI search visibility metrics and KPIs goes deeper on what to measure.

Here's the honest version of the job: an AI SEO agency tries to make your brand look, to an AI, the way a credible expert source looks to a human researcher.

How is this different from traditional SEO?

Traditional SEO optimizes for ranking algorithms. AI answer optimization targets retrieval-augmented generation (RAG) pipelines and model training data. Genuinely different targets.

Google's ranking algorithm weighs hundreds of on-page and off-page signals and spits out a ranked list of URLs. A language model answering a question either retrieves content from an index in real time (the way Perplexity does) or draws on patterns baked into training data (the way ChatGPT often does). Getting cited in the first case is close to traditional SEO. Getting cited in the second is more about how often and how authoritatively your brand appeared across the web before the model's training cutoff.

A 2024 study from Princeton and Georgia Tech found that AI-generated search answers tend to favor sources already high-authority in traditional web rankings, but the correlation isn't clean. Pages with strong topical specificity and clear factual claims got cited even when their domain authority was modest [1]. That gap is the window agencies are trying to open.

Keyword density matters less here. What matters more: does a piece of content contain a complete, attributable, standalone answer to a specific question? Generative engine optimization is the emerging term for this practice, and it's worth learning the vocabulary before you hire anyone.

One more thing. Traditional SEO has years of case studies, algorithm documentation, and benchmarks. AI SEO has almost none of that. Anyone claiming certainty about what works is probably overselling.

How much does an AI SEO agency cost?

Nobody has clean industry-wide pricing data on this yet. Based on publicly posted rates and reported retainers from agencies that have announced AI search practices, the range looks like this:

| Service tier | Typical monthly cost | What's usually included | |---|---|---| | Entry / startup | $1,500 to $3,500 | Content audit, basic schema, monthly report | | Mid-market | $3,500 to $8,000 | Full content strategy, entity building, PR outreach | | Enterprise retainer | $8,000 to $20,000+ | Dedicated team, custom monitoring, technical SEO | | Project-based | $5,000 to $25,000 one-time | Content overhaul or AI visibility audit |

These are directional. An agency in San Francisco with senior staff sits at the top end. A boutique shop or a traditional agency bolting on an AI layer sits lower. Some of the sharpest practitioners right now are solo consultants who charge $200 to $400 an hour and don't call themselves an agency at all.

Watch for agencies that bundle AI SEO into a traditional SEO retainer, charge a premium for the AI piece, and can't show you what AI-specific work they're doing. Ask for a sample deliverable before you sign anything.

Platform tools that let you run your own AI visibility monitoring cost anywhere from free (limited) to $500 to $2,000 a month for full-featured SaaS products. AI SEO tools covers the tool landscape, so you can separate what you can do yourself from what needs agency help.

Estimated monthly cost by AI SEO agency tier

| | | |---|---| | Entry / startup tier | $2,500 | | Mid-market tier | $5,750 | | Enterprise retainer | $14,000 | | Project-based (one-time) | $15,000 |

Source: Reported industry pricing ranges, compiled 2024-2025

What results can you realistically expect?

Be skeptical of guarantees. Nobody can promise ChatGPT will mention your brand, because nobody outside OpenAI fully controls what ChatGPT says. What an agency can reasonably promise: structured content that is more likely to be retrieved, entity signals that make your brand recognizable to AI systems, and monitoring to tell you whether citations are climbing.

A 2023 analysis by the Tow Center at Columbia Journalism School found that AI news summaries cited a small set of high-authority domains over and over, with the top domains taking a lopsided share of citations [2]. That's discouraging if your brand is new. It's encouraging if you have any existing authority, because the gap between zero citations and occasional citations closes with the right content work.

Timelines are genuinely unclear. Traditional SEO often shows movement in three to six months. AI visibility work can show up faster in retrieval-based engines like Perplexity, which indexes content in near real time [9], and much slower in models that lean on training data, where your content needs to exist and be authoritative before the next training run, which could be months or years out. That distinction matters when you set expectations with your board.

The most honest thing any agency can tell you: we can move retrieval-based AI search in 60 to 90 days, and training-data visibility on a longer timeline we don't fully control.

What should you look for when hiring an AI SEO agency?

Start by checking whether they can show you actual AI citation examples for clients. Screenshots of a Perplexity or ChatGPT response naming a client brand, before and after their engagement, are the closest thing to a proof point this industry has. No examples? That's a real problem.

Second, ask how they measure success. If the answer is "organic traffic" or "keyword rankings" and nothing else, they haven't shifted to AI search thinking. You want mention rate, citation share, and prompt-response analysis in the reporting. AI visibility tools show what good measurement looks like before you're locked into an agency's proprietary dashboard.

Third, check whether they understand the differences between engines. Perplexity's retrieval mechanism is not Google's AI Overviews, which is not how ChatGPT pulls context. An agency that treats them identically is running one template instead of doing engine-specific work. Google's AI Overviews, for example, lean heavily on Google's existing index and structured data, so traditional technical SEO overlaps there [3].

Fourth, be wary of anyone selling themselves as a "top AI SEO agency buzz dealer." That positioning is marketing, not substance. The good shops are usually quieter and more specific about their methodology.

Fifth, make sure real writers and content strategists are on staff, more than SEO technicians. The content itself is the product that gets cited. Thin on editorial talent, and the output shows it.

What's the difference between AI SEO and GEO (generative engine optimization)?

Generative engine optimization is the term academics and practitioners use for the specific practice of optimizing content to appear in AI-generated answers [4]. AI SEO is the broader market label that includes GEO but also covers AI-assisted SEO tools, AI-driven content production, and AI-powered technical audits.

When an agency says "AI SEO," they could mean any of those, which is part of why the category is muddy. The services that matter most for brands trying to appear in AI answers are what you'd call GEO: content structure, entity signals, citation authority, and schema markup.

Some agencies now use GEO as their primary term to signal they're focused on AI answer engines rather than using AI tools to do traditional SEO faster. If precision matters to you, ask directly: "Are you helping me rank in Google, or helping me get cited by AI assistants?" Related goals, but the tactics and measurement differ enough that you want clarity upfront.

For a fuller breakdown of the GEO concept and how it maps to specific engines, generative engine optimization is worth reading before your first agency call.

How do AI search engines decide which brands to cite?

The honest answer is that the exact mechanisms aren't fully public. But researchers and practitioners have spotted a few consistent patterns.

Retrieval-augmented engines like Perplexity and Google's AI Overviews pull from web content at query time. For those, the factors look a lot like traditional SEO: topical relevance, freshness, domain authority, structured data. Google has said AI Overviews draw on the same signals used for core web ranking [3].

For models answering from training data, the picture gets messier. A paper from researchers at Princeton, the University of Washington, and AI2 found that language models tend to reproduce facts and entity mentions that appeared frequently and consistently across diverse sources in their training corpus [5]. So if your brand shows up in 50 credible publications saying roughly the same thing about what you do, a model is more likely to cite you than if you have 1,000 blog posts on your own domain.

Recency is a real factor for retrieval engines and nearly irrelevant for training-data answers, where your content has to exist before the training cutoff [10].

One underrated factor: specificity. Models are more likely to cite a source with a precise, standalone claim ("the average cost is X" or "the process has three steps") than one that is generally positive about a brand. Write to be quoted, not to be admired.

You can watch these factors play out by studying real citation patterns with tools like the ones covered in brandrank.ai visibility insights analysis.

Should you hire an agency or build this capability in-house?

This comes down to team size, budget, and how central AI search visibility is to your growth.

In-house works if you already run strong content and SEO operations and you're willing to invest in training and tooling. The core skills (content strategy, entity SEO, structured data, digital PR) are all learnable. The AI-specific monitoring layer needs either a SaaS tool or custom engineering.

An agency works if you don't have dedicated SEO headcount, if you need speed, or if you want an outside team running ongoing competitive monitoring. Agencies that have been at this since 2023 have pattern-matched across industries and can move faster than an internal team starting cold.

A hybrid works well for mid-sized companies: bring in an agency for a 90-day audit and strategy sprint, then hand the playbook to an internal content team to run. That's probably the best value for most brands in the $10M to $100M revenue range.

Before you commit either way, running an AI visibility audit is a reasonable first move. Spawned offers one if you want a structured starting point for seeing where your brand stands across AI engines today.

The one clear waste of money: paying an agency a premium for AI SEO when they're doing nothing beyond what a traditional SEO retainer already covered. Get specifics.

Which AI engines should your agency be optimizing for?

The engines that matter right now: Google AI Overviews, ChatGPT (with and without Browse), Perplexity, Gemini, and Claude. Each has a different retrieval mechanism and a different user base.

Google AI Overviews is the highest-volume surface by a wide margin, since it sits at the top of regular Google results for a big share of queries. Google hasn't published the exact percentage, but a 2024 BrightEdge study found AI Overviews appearing on 7 to 15 percent of all U.S. queries, with much higher rates for informational searches [6]. For most brands, this is where AI search optimization moves the most traffic in the near term.

Perplexity is smaller but growing fast, and its retrieval-based model means content work can show up in weeks instead of months. It skews toward tech-forward audiences and researchers, which matters depending on your market.

ChatGPT's browsing mode matters for product and service queries [10]. ChatGPT without browsing matters if your brand is prominent enough to sit in training data. For most brands, that's the longer game.

Gemini and Claude are worth watching but are smaller surfaces for most commercial queries today.

A good agency has explicit strategies for each and won't run one approach across all of them. Google AI search and AI search cover the engine-specific landscape in more depth.

What does AI SEO content actually look like?

Good AI-optimized content is clear, specific, and built to be quoted in fragments. That doesn't mean robotic. It means every section answers a real question completely enough that an AI can lift the answer without the surrounding context.

A few concrete patterns show up in content that gets cited.

Direct answer first, always. The answer to the question in the heading lands in the first two sentences, not buried in paragraph four. AI retrieval systems favor content where the answer sits near the question.

Named sources and attributed numbers. "Studies show" is not quotable. "A 2024 Stanford study found X" is. Include the who and the when whenever you cite a fact.

FAQ sections with full standalone answers. FAQs are one of the most reliably cited formats in AI responses. Each answer has to stand alone without the question above it.

Clean schema markup. FAQ, HowTo, Article, and Product schema make it easier for retrieval systems to extract structured answers. This is technical work a good agency handles, but you can audit your own pages with Google's Rich Results Test [7].

Short, linkable definitions. Define a term clearly and briefly, and models often pull that definition and credit the source. "X is Y" sentences are overrepresented in AI citations.

This is why AI SEO is ultimately a content quality problem. The agencies that get results are the ones with strong editorial judgment, more than technical SEO chops.

Are there risks or downsides to AI SEO agency work?

A few real ones.

The measurement problem. There's no Google Search Console equivalent for AI citation volume yet. Agencies often report proxy metrics (content quality scores, schema coverage, third-party mention counts) instead of actual AI answer appearance rates. Some newer tools are closing the gap, but it's early. Understand what you're measuring before you sign a year-long retainer.

The attribution problem. If your brand starts showing up more in ChatGPT, it's hard to prove the agency caused it. Maybe you landed a favorable press mention. Maybe a competitor had a bad news cycle. Agencies claiming precise attribution are stretching.

Algorithm volatility. Google changes AI Overviews behavior often. OpenAI changes what ChatGPT retrieves. Perplexity's ranking signals shift. A strategy that works in Q1 can underperform in Q3. Good agencies flag this risk. Bad ones don't.

Content saturation. If every brand in your category suddenly ships the same structured, FAQ-heavy content, the signal loses value. Early movers have a real edge, but it won't hold forever.

None of these are reasons to avoid the category. They're reasons to hire agencies with clear measurement frameworks and honest talk about uncertainty.

How do you measure whether your AI SEO investment is working?

The cleanest metric is mention rate: how often does your brand appear when you submit a fixed panel of relevant prompts to a given AI engine? Run the same 50 to 100 prompts monthly, record whether your brand is cited, and track the trend.

Under that, track citation share (your mentions versus competitors'), position in the response (are you named first or fifth?), and whether the mention reads positive, neutral, or negative in context.

For retrieval-based engines like Perplexity and Google AI Overviews, organic traffic from those surfaces is a real, trackable signal. Google Search Console reports traffic that can be filtered to isolate AI Overviews clicks [8].

Spawned's platform is built for exactly this kind of ongoing monitoring across multiple AI engines, worth a look if you want structured dashboards instead of manual prompt testing.

For a full breakdown of what to track and how to weight it, AI search visibility metrics and KPIs lays out a practical framework.

One rule above the rest: establish a baseline before any agency work starts. No before snapshot, no way to prove progress. Sounds obvious. A surprising number of engagements start without one anyway.

Sources

1. Liu et al., Princeton / Georgia Tech, 2024 (arXiv preprint)
2. Columbia Journalism School, Tow Center for Digital Journalism
3. Google Search Central, AI Overviews documentation
4. Aggarwal et al., 'GEO: Generative Engine Optimization', arXiv 2023
5. Mallen et al., Princeton / UW / AI2, 'When Not to Trust Language Models', ACL 2023
6. BrightEdge Research, AI Overviews prevalence study, 2024
7. Google Rich Results Test, search.google.com
8. Google Search Console Help, AI Overviews reporting
9. Perplexity AI, company blog / documentation
10. OpenAI, ChatGPT browsing feature documentation