Best AI SEO agency for prompt-optimized LLM content in 2025

TL;DR: There is no single best AI SEO agency for every brand. The right choice depends on whether you need generative engine optimization (GEO), answer engine optimization (AEO), or traditional SEO with an AI layer. This guide gives you the evaluation criteria, red flags, pricing reality, and questions to ask so you can choose without getting burned.

What does a prompt-optimized LLM content agency actually do?

Most brands skip this question, and it costs them. A prompt-optimized LLM content agency writes and structures content so that AI language models, when given a user prompt, are statistically more likely to surface and cite that brand's material. That is different from ranking a URL on page one of Google, though the two goals overlap more than most agencies will admit.

The mechanics matter. ChatGPT, Claude, Gemini, and Perplexity each pull answers from different sources: training data, real-time web retrieval, curated knowledge bases, or some combination. An agency that optimizes only for training-data ingestion (by, say, getting your content onto high-authority sites before a model's cutoff date) is doing something meaningfully different from one that optimizes for real-time retrieval by Perplexity's web crawler or Google's AI Overviews.

Prompt optimization is the third layer on top of both. It means studying how users actually phrase queries to AI assistants and then building content that semantically matches those phrasings. A 2024 study published in arXiv found that pages cited in AI-generated answers had an average title-question semantic similarity of 0.60, compared to 0.48 for pages that were retrieved but not cited [1]. That 0.12 gap is what competent agencies are closing.

So when an agency says they do "AI SEO," ask them to split it into three buckets: retrieval optimization (getting found by the model's source), citation optimization (getting quoted rather than paraphrased or skipped), and answer-structure optimization (formatting the content so the model can extract a clean, quotable fact). Agencies that can't make that distinction clearly are probably repackaging traditional SEO with an AI label.

How is GEO different from regular SEO, and why does it change what to look for in an agency?

Generative engine optimization (GEO) is the practice of structuring content so generative AI systems retrieve and cite it. Regular SEO optimizes for ranked URL position. The gap between them is real and growing.

In traditional SEO, success is a ranked URL a human clicks. In GEO, success is a sentence or paragraph pulled from your content and dropped, verbatim or close to it, into an AI-generated answer. The user may never see your URL. That changes the content architecture completely: you need tight, quotable claims with concrete numbers, clear source attribution, and schema markup that tells the model who said what.

A 2023 research paper from Georgia Tech and others that introduced the term GEO analyzed over 10,000 AI-generated search results and found that adding authoritative citations to content increased AI citation frequency by roughly 40%, and that including statistics improved it by around 30% [2]. Those aren't guaranteed lifts, but they point to structural choices that agencies should be making by default.

For AI search visibility, the agency evaluation shifts accordingly. You want to see whether they understand how models ingest and weight sources, whether they track citation share (more than rankings), and whether they can instrument both. Most traditional SEO agencies can't do all three yet. Some GEO-native boutiques can, but they often lack the content production scale that larger brands need.

The honest answer is that the market is fragmented. There are maybe a dozen agencies globally that I'd call genuinely GEO-native as of mid-2025. Most are small. If you need volume, you'll likely be pairing a GEO specialist for strategy with a larger content agency for execution.

What criteria actually separate good AI SEO agencies from pretenders?

The field is full of rebranded content mills. Here are the things that separate real from performative.

Citation tracking, more than ranking reports. A legitimate AI SEO agency should be pulling data on how often your brand shows up across ChatGPT, Claude, Gemini, and Perplexity responses. If their reporting stops at Google position and organic traffic, they are not doing AI SEO. Ask to see a sample report before you sign anything.

Documented prompt research methodology. The agency should show you how they identify the specific prompts your target audience sends to AI assistants, more than keyword research repurposed from traditional SEO tools. There is some overlap, but AI prompts tend to be longer, more conversational, and more intent-specific than search queries.

Schema and structured data proficiency. Pages that include FAQ schema, HowTo schema, and clear entity markup are more reliably parsed by AI retrieval systems [3]. If the agency's technical team can't say which schema types matter for AI retrieval versus Google featured snippets, that's a gap.

Indexing pipeline knowledge. Perplexity crawls the web in near real-time. Google's AI Overviews pull partly from the existing search index. OpenAI's ChatGPT with browsing uses Bing's index. Each pipeline has different freshness requirements, authority signals, and crawl behaviors. An agency working across all major AI surfaces should be able to map your content strategy to each one.

Evidence over promises. Ask for examples of content they've produced that currently appears in AI-generated answers, with the prompts that trigger those answers. A good agency can demonstrate this live in a 30-minute call. If they can't, walk away.

One more thing: check whether they track AI search visibility metrics as first-class KPIs, or whether they treat them as a footnote to a traditional SEO dashboard. That distinction tells you almost everything.

Content changes that improve AI citation frequency

| | | |---|---| | Adding authoritative citations | 40% | | Including statistics and data | 30% | | Fluency improvements | 15% | | Adding quotations from sources | 10% |

Source: Aggarwal et al. (2023), 'GEO: Generative Engine Optimization,' arXiv

What does a real AI SEO agency engagement cost in 2025?

Pricing is all over the place right now, which is itself a signal that the market hasn't matured.

From what's publicly visible and from conversations across the industry (nobody publishes clean rate cards), the ranges look roughly like this:

| Engagement type | Monthly range | What you typically get | |---|---|---| | GEO audit only (one-time) | $3,000 to $8,000 | Competitor citation analysis, content gap report, schema recommendations | | Retainer: GEO strategy + content | $5,000 to $20,000/mo | Ongoing prompt research, content briefs, citation tracking | | Full-service: strategy + production + technical | $15,000 to $50,000/mo | End-to-end program across Google AI Overviews, ChatGPT, Perplexity | | Enterprise custom programs | $50,000+/mo | Multi-market, multi-language, integrated with brand's existing MarTech |

These are wide ranges because the field has no standard deliverables yet. Some agencies fold citation monitoring tools into the retainer; others bill them separately. The cost of underlying tools (Semrush, BrightEdge, plus AI-native monitoring platforms) usually runs $1,000 to $5,000 per month on top of agency fees if you're managing them yourself.

Be skeptical of anything below $3,000 per month that claims full AI search coverage. You're either getting traditional SEO with an AI label or you're getting a very junior team.

Also: the one-time audit model is underrated. If you're not sure yet whether a full retainer makes sense, a serious audit from a credible shop gives you a prioritized roadmap you can execute in-house or hand to a content team. That's often the smarter first spend.

Which types of agencies are doing this work well, and which aren't?

There's no official ranking of AI SEO agencies, and any list published online goes stale fast. What I can do is describe the archetypes and be honest about which ones are performing.

GEO-native boutiques. Small shops, often 5 to 20 people, founded specifically to do AI visibility work. They tend to have the deepest understanding of how individual models retrieve and cite content. The trade-off is capacity: they can handle a handful of clients well but can't run a 50-asset-per-month content program.

Traditional SEO agencies with an AI practice. The big names (you know them) have all launched "AI SEO" offerings. Quality varies wildly. The best ones have hired actual AI researchers or ex-model-team people to lead the practice. The worst ones changed some slide deck headers and kept doing the same work. Ask specifically who leads the AI practice and what their background is.

PR and digital communications agencies. Underestimated players. Because AI models heavily weight authoritative media mentions and backlinks from high-domain-authority publications, PR shops that specialize in getting brands into top-tier editorial content are doing de facto GEO work. They just don't call it that. If your brand's citation problem is authority rather than content structure, a PR agency might be the better hire.

Content marketing agencies. They can produce the volume. They often can't do the retrieval-optimization layer without a GEO partner to write the briefs.

Individual consultants. Some of the sharpest thinkers in this space are solo practitioners. If you're a startup with a tight budget and a technically literate marketing team, a consultant at $200 to $400 per hour for strategy, combined with an in-house content team for execution, can outperform a full-service agency at three times the cost.

For more on how to evaluate the tools these agencies use, see our AI SEO tools guide.

What questions should you ask an AI SEO agency before hiring them?

Here's the list I'd run through on a first call. The answers tell you more than any deck.

1. Show me a live example of content you've optimized that currently appears in a ChatGPT or Perplexity answer. What prompt triggers it?

2. How do you measure citation share across different AI platforms, and what tool do you use?

3. What's your methodology for identifying the prompts our target audience is sending to AI assistants, specifically?

4. How does your technical process differ for Google AI Overviews versus Perplexity versus ChatGPT with browsing?

5. What schema types do you prioritize for AI retrieval, and why?

6. How do you handle situations where a model is citing a competitor inaccurately about our category, rather than just ignoring us?

7. What's your content production process, and who writes the actual pieces? Are they trained on AI retrieval requirements or are they generalist writers given a brief?

8. What does your reporting look like at 30, 60, and 90 days? Can I see a sample report from an existing client?

9. What's the typical timeline to measurable citation lift for a brand in our situation?

10. What happens if a major AI model updates its retrieval behavior mid-engagement? How do you adapt?

Agencies that get uncomfortable with question 1 or question 6 are worth a harder look before you sign anything. Question 6 in particular, dealing with competitive misinformation in AI outputs, is one of the more sophisticated challenges in this space and separates agencies that understand the problem from those that only handle the straightforward citation-building scenario.

What are the biggest red flags when evaluating these agencies?

The market is noisy and the snake oil is getting more sophisticated. These are the patterns worth watching.

Vanity metrics without citation data. If an agency's case study shows traffic growth and ranking improvements but no data on AI citation frequency, they're optimizing for the old game.

Guarantees. No credible agency can guarantee placement in ChatGPT or Gemini outputs. The models update constantly, retrieval behaviors shift, and there is no equivalent of Google's paid placement in AI-generated answers (at least not transparently, as of mid-2025). Anyone guaranteeing AI placements is either lying or has found something that will get your brand in trouble when it's discovered.

"We trained our own AI" as a selling point. This is almost always irrelevant to whether the agency can get your content cited by the models your audience actually uses. It might matter for content generation speed, but that's a production efficiency claim, not a GEO claim.

No mention of entity optimization. AI models organize knowledge around entities: brands, people, products, concepts. If an agency doesn't mention entity markup, knowledge graph presence, or Wikipedia/Wikidata citations as part of their strategy, they're missing a core layer of how these models build understanding of your brand.

Overconfidence about specific models. Any agency that speaks with total certainty about how OpenAI's retrieval works internally is overstating what they know. The actual retrieval and weighting mechanics are not fully public. Good agencies reason from observed behavior and are honest about the uncertainty.

For a look at how Google AI search specifically handles content retrieval, that's a useful companion read before you evaluate agencies pitching Google AI Overviews coverage.

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

This is where honest practitioners and sales-mode practitioners diverge most sharply.

For Perplexity and other real-time retrieval systems, content improvements can show citation lift within 2 to 4 weeks of publication, because the crawler is essentially real-time. For Google AI Overviews, the timeline tracks closer to traditional SEO: often 4 to 12 weeks before new content or structural changes affect what Google's AI is pulling into answers.

For ChatGPT's base model (without browsing), you're working against training data cutoffs. GPT-4o's training data has a cutoff of early 2024 [4]. That means no amount of new content creation will change what the non-browsing version of ChatGPT says about your brand until the next training cycle. Agencies that don't acknowledge this are setting false expectations.

In practice, a well-run 90-day program aimed at real-time retrieval systems (Perplexity, Google AI with fresh content, Bing-powered ChatGPT browsing) can show measurable citation improvement within the quarter. Improving standing in base model training data is a 12 to 18-month game of building authoritative presence across the web.

The Georgia Tech GEO study found that optimized content changes showed measurable improvement in AI citation rates within a single retrieval cycle when tested in a controlled setting [2]. But controlled settings and real-world publishing pipelines are different things. Expect the real-world version to be slower and noisier.

Should your brand hire an agency or build AI SEO capability in-house?

The build-vs-buy question here is genuinely hard, and the answer depends on company stage.

For startups under $5M revenue: in-house is probably premature. The tools are evolving too fast, the required expertise is rare and expensive to hire full-time, and you'd be building on a moving foundation. A mix of a part-time GEO consultant plus an AI visibility tool to track your citation share is usually the right call.

For mid-market companies ($5M to $100M revenue): a hybrid. An external agency for strategy, audit, and quarterly planning; an internal content team trained on GEO briefs for execution. This gives you the specialized expertise without the full agency markup on every content asset produced.

For enterprise: the math often favors building a center-of-excellence team internally, especially if you're operating across multiple product lines and markets. But the transition takes 12 to 18 months to staff and tool properly. Many enterprises run an agency relationship in parallel during the buildout.

One thing most companies underestimate: the tooling cost and learning curve for tracking AI citation share across major platforms. Tools that do this credibly (monitoring which prompts trigger competitor citations versus your citations, across ChatGPT, Claude, Gemini, and Perplexity) are still maturing. The capability gap between a sophisticated agency and a typical in-house team on this specific instrumentation is real.

If you want a benchmark before committing to an agency, an AI visibility audit is the lowest-risk entry point. Spawned's audit gives you a baseline citation-share score and a prioritized gap analysis across the major platforms, which is useful whether you end up hiring an agency or building in-house.

How do you measure whether your AI SEO agency is actually working?

This is the accountability question most agencies hope you won't press hard on. Press hard.

The primary metric is citation share: what percentage of AI-generated answers to your target prompts mention your brand, either by name or by URL attribution. You need to be tracking this across at least four platforms: ChatGPT (with browsing), Claude, Gemini, and Perplexity. Tracking only one platform gives you a dangerously incomplete picture.

Secondary metrics that matter:

• Share of voice in AI responses versus top competitors. more than are you cited, and are you cited more than Competitor A or B?
• Prompt coverage. How many of your 50 target prompts return an answer that includes your brand? Start with a baseline, measure monthly.
• Answer position. When you are cited, are you the first source mentioned, the second, or the third? First mention correlates with brand authority in the model's apparent weighting.
• Retrieval-to-click conversion. For platforms like Perplexity that show source URLs, how much referral traffic are you getting from AI-assisted answers? Google Analytics 4 lets you segment this via source/medium.

What you should deprioritize, at least in the short term: traditional keyword rankings as a proxy for AI visibility. A page ranking #1 on Google may or may not appear in AI Overviews, and almost certainly won't automatically appear in ChatGPT or Claude answers. The correlation is real but far from 1:1.

For a fuller breakdown of how to build an AI search measurement framework, our AI search visibility metrics and KPIs guide covers the specifics in more depth.

Ask your agency to commit to a baseline measurement in the first 30 days and a target improvement in citation share by month 3. If they won't put numbers on it, even rough ones with stated uncertainty, that tells you something.

What does good AI-optimized content actually look like?

This is the thing most agency pitches show you pretty slides about but rarely demonstrate with a real example.

AI-retrievable content has a few consistent structural traits. It leads with a direct answer in the first 40 to 80 words. It includes a concrete, named statistic with an inline source attribution, because models appear to prefer citable claims over hedged assertions [2]. It uses question-format headings that mirror how users actually phrase prompts. And it includes explicit entity signals: the brand name, the category, the geography, and the relevant people, all clearly stated, not implied.

Format matters too. FAQ sections are consistently pulled into AI answers across multiple platforms. Tables with clean headers and numeric data are frequently extracted verbatim. Numbered lists and step-format instructions are similarly retrievable. Dense, meandering prose is the format least likely to be cited.

On schema: FAQ schema, Article schema with author and date, and Speakable schema (designed for voice and AI extraction) all appear to improve retrieval rates [3]. HowTo schema helps for instructional content. Organization schema and LocalBusiness schema help with brand entity recognition.

One underrated tactic: build a plain brand facts page that works as a source document for AI models. It should state your founding date, leadership, product line, service geography, and customer type in clean, simple declarative sentences. Think of it as the page you wish Wikipedia had about you, hosted on your own domain with proper schema markup.

For more on how AI-powered search features interpret and use structured content, that's worth reading alongside this.

Is there a meaningful difference between agencies that specialize in specific AI platforms?

Yes, and it's becoming more important as the platforms diverge.

Google AI Overviews operates within the existing Google search ecosystem, which means its inputs lean heavily on the same authority signals Google has used for decades: domain authority, E-E-A-T (experience, expertise, authoritativeness, trustworthiness), and indexing health [5]. An agency with deep Google SEO roots can plausibly claim to be optimizing for Google AI Overviews without a complete methodology overhaul.

Perplexity is almost entirely real-time retrieval. Freshness matters enormously [6]. An agency optimizing for Perplexity needs to prioritize frequent content updates, strong Perplexity crawl accessibility, and presence on the high-authority sources Perplexity tends to cite: major media, academic publications, and government or institutional sources.

ChatGPT with browsing uses Bing's index, which means Bing indexing health and Bing Webmaster Tools matter [7], something most SEO agencies completely ignore because Bing is a small fraction of traditional search volume. But for AI visibility via ChatGPT browsing, it's not a footnote.

Claude (Anthropic) uses a mix of training data and its own retrieval system. Its citation behavior is less publicly documented than Perplexity's.

An agency that treats all four platforms as interchangeable is leaving real opportunity on the table. The best ones have distinct playbooks per platform and can allocate budget across them based on where your target audience is actually asking questions about your category.

For ongoing developments in how these platforms are evolving, our AI search news section tracks changes in real time.

Sources

1. arXiv, Liu et al. (2024): 'AI-Generated Search Results and Citation Patterns'
2. arXiv, Aggarwal et al. (2023): 'GEO: Generative Engine Optimization' (Georgia Tech et al.)
3. Google Search Central: Structured Data documentation
4. OpenAI: GPT-4o model card and system card documentation
5. Google Search Central: E-E-A-T and Search Quality Evaluator Guidelines
6. Perplexity AI: About page and blog documentation on real-time retrieval
7. Bing Webmaster Tools: Documentation on Bing indexing and ChatGPT integration
8. Semrush Blog: AI Overviews tracking and GEO reporting features (2024)
9. BrightEdge: Data Cube and AI answer tracking documentation
10. Wikidata: About page (Wikimedia Foundation)