Agencies specializing in answer engine optimization: how to find and vet one

TL;DR: Answer engine optimization (AEO) agencies help brands get cited by ChatGPT, Claude, Gemini, and Perplexity. The field is new enough that most shops are former SEO agencies that pivoted; genuine specialists are rare. Expect monthly retainers of $3,000 to $15,000, with ROI measured in citation share and mention frequency, not keyword rankings.

What does an AEO or GEO agency actually do?

Answer engine optimization is the practice of shaping how AI assistants, AI-powered search engines, and large language models describe, recommend, and cite your brand. A genuine AEO agency, sometimes called a generative engine optimization (GEO) agency, does a few things that traditional SEO firms have never had to think about.

First, they audit what AI models currently say about your brand, your category, and your competitors. This means running structured prompt tests across ChatGPT (GPT-4o), Claude, Gemini, and Perplexity, logging the outputs, and finding where you're absent, misrepresented, or beaten by a competitor that shouldn't be beating you. That audit is the diagnostic layer.

Second, they fix the underlying reasons AI models ignore or mangle your brand. That usually means rewriting key pages so they contain clear, factual, cite-ready statements, building FAQ and Q&A content that mirrors the exact questions users ask AI assistants, and making sure high-authority third-party sites (publishers, Wikipedia-adjacent references, data sources the models actually ingest) carry accurate information about you.

Third, they track results over time. This is the piece most agencies are still figuring out, because there's no Google Search Console equivalent for LLM citation share. Reputable shops either build their own prompt-testing infrastructure or use AI visibility tools and brand monitoring software to measure mention rate, sentiment, and citation context.

The work overlaps heavily with generative engine optimization, which is the same discipline with slightly different labeling depending on who you ask [1].

Is AEO different from SEO and GEO, or just a rebrand?

This is the right question to ask any agency pitching you. The honest answer: there's real overlap, but AEO is more than SEO wearing a new hat.

Traditional SEO targets crawl-and-index systems. You optimize a page so Google's crawler finds it, understands it, and ranks it for a query. The feedback loop is fast, quantified, and well-understood. AI SEO and AEO target a different mechanism. LLMs don't crawl your site in real time. They have knowledge cutoffs, they weight information based on what appeared prominently across many sources during training, and they apply their own reasoning layers on top of retrieval [2].

A 2023 Princeton and Georgia Tech study on GEO found that certain content interventions (citing sources, adding quotations, adding statistics, and using fluent authoritative language) increased the visibility of a source's content in AI-generated responses by up to 40% [1]. Keyword density and backlink anchor text, the old SEO staples, had almost no effect on whether an LLM cited a source.

So yes, AEO is a distinct discipline. That said, agencies that don't understand SEO well tend to struggle with AEO too, because a lot of the foundational work (structured data, clear prose, topical authority) still matters. The best AEO shops have strong SEO chops and have deliberately added LLM-specific skills on top.

GEO, AEO, and LLMO (large language model optimization) are largely synonymous in practice. Don't let an agency charge you a premium just for using a different acronym.

What should you look for when evaluating AEO agencies?

The field is new enough that almost anyone can claim expertise. Here's what separates agencies that will actually move your citation metrics from ones that will produce a nice deck and not much else.

They can show you a real before-and-after audit. Ask to see an example audit from a real client (redacted is fine). It should show specific prompt tests, current outputs from multiple AI engines, and a diagnosis of why citations are low. If they can't produce this, they're guessing.

They have a documented testing methodology. Good AEO shops test across at least four platforms: ChatGPT, Claude, Gemini, and Perplexity. They run dozens of prompt variants per category, more than two or three branded queries. They re-run tests consistently so they can detect change over time.

They understand knowledge cutoffs and training data. LLMs have knowledge cutoffs, and the path from "a piece of content exists" to "an LLM cites it" can take months or longer, depending on when a model was last trained or updated. Any agency promising fast results from content alone is either selling a tool that uses RAG (retrieval-augmented generation, which is real-time) or overselling.

They separate owned content work from earned third-party work. Getting AI models to cite you requires both: well-structured, factual owned content that's easy to extract information from, and third-party mentions on sources that models weight heavily (Reuters, Wikipedia, government databases, major trade publications). An agency focused only on your own site is leaving half the job undone.

They can explain how they measure success. Ask them directly: what KPIs do you track, and what tools do you use? AI search visibility metrics are an evolving area. Reputable shops will give you an honest answer about what's measurable right now and what isn't, rather than promising a dashboard that shows everything.

One red flag worth calling out explicitly: any agency that says AI citation is just about schema markup. Schema helps, but it's one small piece of a much larger picture.

Content interventions ranked by AI citation visibility improvement

| | | |---|---| | Adding statistics and data | 40% | | Adding citations / references | 37% | | Adding authoritative quotations | 35% | | Improving prose fluency | 17% | | Adding keyword-rich content | 5% | | Traditional SEO keyword density | 2% |

Source: Aggarwal et al., GEO: Generative Engine Optimization, Princeton / Georgia Tech, 2023 (arXiv:2311.09735)

What do AEO agencies charge, and what are you actually buying?

Pricing is all over the map right now because the market hasn't matured. Based on publicly available agency pricing pages and industry surveys through 2024-2025, here's a rough picture.

Smaller boutique shops with genuine AEO focus typically start at $3,000 to $6,000 per month for a retainer that includes an initial audit, ongoing content optimization, and monthly citation tracking. Mid-size agencies with dedicated GEO practices run $6,000 to $15,000 per month. Enterprise-level engagements at large digital agencies can reach $20,000 to $50,000 per month, though at that price you're usually bundling in SEO, content, PR, and paid media [3].

Project-based work is also common for the audit phase. A standalone AEO audit, the kind that tells you where you stand across AI platforms and what's causing gaps, runs roughly $5,000 to $20,000 depending on the scope of the competitive analysis and how many prompt categories you test.

What are you actually buying? The honest breakdown:

• Audit and strategy (one-time or quarterly): Diagnostic prompt testing, gap analysis, competitive benchmarking, a content and PR roadmap.
• Content optimization (ongoing): Rewriting existing pages, creating new FAQ and definitional content, adding structured data, optimizing for entity clarity.
• Earned media and third-party placement (ongoing): Outreach to publishers, Wikipedia editing where policies allow, data partnerships, press coverage designed to land on sources AI models trust.
• Tracking and reporting (ongoing): Monthly citation share reports, sentiment analysis, alert monitoring.

Be skeptical of agencies that roll all of this into one vague line item labeled "AEO management." You want to know specifically what work is happening each month.

Which types of agencies are doing this work in 2025?

The agency landscape for AEO breaks down into four rough categories right now.

Specialist AEO/GEO boutiques. A small number of agencies built specifically around AI visibility have emerged since 2023. They tend to be 5-30 person shops, often founded by former SEOs or NLP researchers. They move fast, have strong diagnostic tools, and are genuinely ahead of the curve. The tradeoff is less operational scale if you need high-volume content production.

SEO agencies with a GEO practice. The majority of agencies pitching AEO are established SEO shops that added a GEO or AEO offering in 2024-2025. Quality varies enormously. Some have genuinely invested in new tooling and staff. Others repackaged their existing content work with new terminology. Ask them when they started doing this work and what prompted it.

Full-service digital agencies with an AI track. Large integrated agencies (think 100+ person shops that handle SEO, paid, creative, and PR) increasingly have an AI visibility practice. These are good fits for enterprise brands that want one agency relationship, but AEO may not be the team's primary focus.

PR and communications firms pivoting to AI visibility. Because third-party mentions matter so much to LLM citation, some PR firms now position themselves as AEO specialists. Their strength is earned media and publication placement. Their weakness is usually the technical and content optimization side. A PR firm alone won't cut it; you need the content work too.

For most mid-market brands, the best option is either a specialist boutique or an SEO agency that can demonstrate genuine GEO capability. If you want to see what credible AI-focused work looks like before hiring anyone, tools like Brandrank.ai visibility insights can give you a baseline read on your current AI citation footprint.

How do you measure whether an AEO agency is working?

This is where honest agencies separate from the ones that will drown you in activity metrics. Here's what actually matters.

Citation frequency. How often does your brand appear in AI-generated answers to relevant category queries? You want this measured consistently, across the same set of prompt variants, so you can see the trend over time. A jump from appearing in 12% of relevant queries to 28% over six months is a real result.

Citation context. Where in the response do you appear? Are you the top recommendation, a secondary option, or a brief mention buried in a list? Being named first in a ChatGPT response carries more weight than appearing seventh.

Sentiment accuracy. What does the AI say about you when it does mention you? Errors, outdated information, or negative associations matter as much as presence.

Share of voice vs. competitors. Your raw citation rate only makes sense relative to the category. If you're in 30% of AI answers but your main competitor is in 70%, you have a problem. This competitive layer is what most agencies call "AI share of voice."

Downstream business signals. AI visibility should correlate with something real: branded search volume, direct traffic, inbound leads attributed to AI channels, or conversion rate changes. The connection isn't always clean, but agencies that never try to tie their work to business outcomes are dodging accountability.

A 2024 study published in Information Processing and Management found that AI-generated search summaries significantly changed how users allocated clicks, with sources cited in AI summaries receiving meaningfully different traffic patterns than those that weren't [4]. That's the business case for tracking this.

The AI search landscape is changing fast, and any honest agency will tell you that measurement methodology will change as the platforms do.

What content and technical work do AEO agencies typically prioritize?

Here are the interventions AEO agencies actually run, ranked roughly by how much evidence exists for their effectiveness.

Definitive, citable factual statements. AI models love content that makes clear, verifiable claims. "Company X was founded in 2012, is headquartered in Austin, and serves 4,000 enterprise customers" is more citable than "Company X is a leading provider of solutions for businesses." Agencies rewrite brand and product pages to be entity-dense and factually specific.

FAQ content that mirrors AI query patterns. Perplexity, ChatGPT, and Gemini all field question-format queries constantly. Pages that answer specific questions in the first paragraph, then elaborate, get extracted more often. The Princeton/Georgia Tech GEO study found that sources adding statistics and citing references saw citation rates improve by 40% or more [1].

Structured data and schema markup. Schema.org markup (Organization, FAQPage, Product, Review) helps AI-adjacent systems, particularly Google's AI Overviews, extract structured information. It's not the whole game, but it's table stakes.

Third-party source authority building. This is the piece most content-only agencies miss. LLMs weight information that appeared prominently across many authoritative sources. Getting accurate, detailed brand information onto Wikipedia (where policies allow), into industry databases, into Reuters or AP-cited press coverage, and into academic or government references is high-value work. It's slow and hard, which is why agencies that do it well charge more.

Knowledge graph and entity optimization. Google's Knowledge Graph feeds several AI systems. Making sure your entity is correctly defined, disambiguated from similar names, and connected to the right attributes and related entities is foundational.

Avoiding AI-generated content at scale. Most serious AEO practitioners warn against flooding the web with AI-generated content in hopes of improving training data representation. Models are increasingly able to detect synthetic content, and Google's helpful content guidance already penalizes it [5]. The agencies worth hiring emphasize editorial quality over volume.

For brands managing this work in-house before or alongside an agency, AI SEO tools can help track which content optimizations are moving citation metrics.

How long does AEO take to show results?

Slower than most marketing channels. Faster than most people expect once you understand the mechanism.

Content changes you make today won't affect a closed model like GPT-4o until it next updates its training data or weights, which can be many months away. Perplexity is different because it uses real-time web retrieval via RAG, so content and source changes can influence Perplexity responses within days or weeks of a major third-party publication picking up your story.

Google's AI Overviews draw from a mix of indexed content and the Knowledge Graph, meaning changes there can surface in weeks rather than months, assuming your site is well-crawled and the content is strong [6].

A realistic timeline for a brand starting from a weak AI citation position:

• Weeks 1 to 4: Audit complete, strategy defined, quick-win content updates shipped.
• Months 2 to 3: Perplexity and AI Overviews citation improvements become visible as new content indexes and third-party placements start appearing.
• Months 4 to 8: Meaningful movement in ChatGPT and Claude citation rates, assuming major model updates have occurred or retrieval layers are active.
• Month 6 onward: Competitive share-of-voice data becomes statistically meaningful.

Anyone promising "results in 30 days" across all AI platforms is either selling Perplexity-only optimization or not being straight with you. The knowledge cutoff problem is real, and every honest practitioner acknowledges it.

What are the red flags that an agency doesn't know what it's doing?

The field is young and credentials are thin, so buyer scrutiny matters more than usual.

They frame AEO as "voice search optimization" rebranded. Voice search was a 2017-era discipline focused on smart speakers and featured snippets. AEO is different in mechanism, target platforms, and measurement. Confusing them suggests the team hasn't actually studied how LLMs work.

Their case studies show keyword ranking improvements, not citation metrics. If every success story is expressed in Google ranking positions, they're doing SEO and calling it AEO.

They promise to "train" ChatGPT or Claude on your content. You cannot submit content directly to OpenAI or Anthropic's model training. That's not how closed models work. Some agencies imply this is possible. It isn't.

They rely entirely on schema markup. Schema is useful, but it's maybe 10% of the solution. An agency whose entire AEO offering is schema implementation is not an AEO agency.

They can't name a knowledge cutoff or explain retrieval-augmented generation. These are basic concepts. If the team leading your engagement can't explain why Perplexity behaves differently from ChatGPT for new content, they're not equipped to do this work.

They have no independent tracking capability. Ask who runs the citation tracking and what tool they use. "We'll check manually" is not an acceptable answer for a paid engagement.

If you want to run your own reality check before a sales call, pulling your brand's current AI citation profile with something like Spawned's AI visibility audit gives you baseline data to bring to the conversation. Any agency worth hiring should be able to explain what they see in that data.

Should you hire an agency or build AEO capabilities in-house?

The honest answer depends on your team's technical depth and your budget.

Building in-house makes sense if you already have a strong content and SEO team that's curious about this space, a budget to add one or two specialists or at minimum access to good tooling, and a product or brand complex enough that an agency would spend significant ramp time understanding your category. Companies in regulated industries (healthcare, finance, legal) often prefer in-house because content accuracy review is easier to control internally.

Hiring an agency makes sense if you have no one internally who understands LLM behavior or has time to run structured citation testing, you need speed (a good agency has infrastructure already built), or you want competitive intelligence across your category that goes beyond your own brand. Agencies running AEO programs across multiple clients in a vertical build pattern recognition that's hard to replicate in-house quickly.

A hybrid is often the best answer for mid-sized companies. Bring in an agency for the audit, initial content strategy, and third-party placement work. Build internal capability for ongoing content execution and tracking using available AI visibility tools. Then reassess at the 12-month mark whether full in-house management is feasible.

One thing to avoid: the all-or-nothing trap. Doing nothing because you can't afford a full agency engagement is a mistake. Even small, well-targeted content improvements, structured prompt tests run with free tools, and a better Wikipedia and third-party presence can move citation metrics meaningfully.

What does the research say about which AEO tactics actually work?

The academic literature is thin but growing. A few studies are worth knowing.

The most-cited is the 2023 Princeton and Georgia Tech paper "GEO: Generative Engine Optimization" by Aggarwal et al. The paper tested nine content interventions on a dataset of 10,000 queries across multiple generative engines. The finding: "adding citations, quotations from relevant sources, and statistics" led to the largest citation improvement, with some interventions increasing source visibility by up to 40% [1]. Keyword stuffing and traditional SEO tactics had little effect.

A 2024 BrightEdge study found that Google AI Overviews cited sources from the top 10 organic results in 99.5% of cases, suggesting that traditional SEO authority still matters as a floor condition for AI Overview visibility [7]. You can't opt out of needing good organic rankings entirely.

Separate research on Perplexity's citation behavior found that sources with strong domain authority, recent publication dates, and clear structured content (headers, lists, tables) were cited more frequently, and Perplexity's own documentation confirms it uses a retrieval layer, not static training data [8].

For Google AI search specifically, Google's own guidance in its Search Quality Evaluator Guidelines names E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) as the framework for quality [5]. Agencies that structure their work around improving demonstrable E-E-A-T signals are aligned with how Google's AI systems evaluate content.

Nobody has clean data yet on the long-term ROI of AEO agency engagements versus in-house or no-investment approaches. The closest proxy is citation share improvement over 12-month windows, and published case studies from credible agencies are starting to appear, but they're still sparse.

How do AEO agencies handle different AI platforms differently?

This is something the better agencies get right that less experienced ones don't: Perplexity, ChatGPT, Gemini, and Claude are not the same optimization target.

Perplexity uses real-time web search with citations. Getting cited there is, in many ways, closer to traditional SEO because it relies on current indexed content, domain authority, and relevance. Publishing well-structured, accurate content on an authoritative domain works. Perplexity also pulls from Reddit, news sources, and niche forums, so your presence in those communities matters [8].

ChatGPT (without browse mode) relies mostly on training data with a knowledge cutoff. Changes to your content affect it only when OpenAI retrains or updates the model. The path to better ChatGPT citations is building long-term authoritative presence on sources that got heavily crawled during training: major publications, Wikipedia, authoritative data sources.

Gemini sits between the two. Google's Gemini in Search uses live retrieval tied to Google's index. Gemini in the standalone app can switch between live search and static knowledge depending on the query. Traditional SEO and new AEO tactics both apply, and organic ranking still matters as a prerequisite.

Claude (Anthropic) for most users is a static model without live retrieval. Similar to pre-browse ChatGPT, citation improvement takes the long path through high-authority source representation over time.

A good AEO agency segments its work by platform and is transparent about which interventions are likely to move which platform's outputs and on what timeline. One-size-fits-all AEO is a warning sign.

You can explore how different AI-powered search features handle your category to get a clearer sense of where your biggest gaps are before engaging an agency.

Sources

1. Aggarwal et al., 'GEO: Generative Engine Optimization', Princeton / Georgia Tech (2023), arXiv:2311.09735
2. Anthropic, Claude model overview documentation
3. Clutch.co, Digital Marketing Agency Pricing Survey 2024
4. Information Processing and Management, 'Effects of AI-generated summaries on user click behavior', 2024
5. Google, Search Quality Evaluator Guidelines (E-E-A-T), Google Search Central
6. Google, How AI Overviews work, Google Search Central
7. BrightEdge, AI Overviews Organic Correlation Study 2024
8. Perplexity AI, How Perplexity Works (official documentation)
9. Search Engine Land, GEO Agency Market Coverage, 2024-2025
10. MIT Sloan Management Review, 'Competing in the Age of AI', 2024