Best LLM SEO companies: how to find one that actually works

TL;DR: LLM SEO (also called GEO or AEO) is a separate discipline from traditional SEO. The best firms combine content structuring, AI citation research, and answer-engine tracking. No single agency dominates yet. This guide gives you the evaluation criteria, the questions to ask, and the red flags to avoid before you write a check.

What is LLM SEO and how is it different from regular SEO?

LLM SEO is the practice of optimizing your brand's content so large language models, specifically ChatGPT, Claude, Gemini, and Perplexity, cite, recommend, or surface your brand when users ask relevant questions. It goes by several names: generative engine optimization (GEO), answer engine optimization (AEO), and AI search visibility. The mechanics differ sharply from traditional SEO.

Traditional SEO is built around crawlability, keyword ranking, and click-through from a ten-blue-links results page. LLM SEO is built around whether an AI's training data and retrieval layer treat your content as authoritative enough to quote. Google's crawler still matters for AI Mode and Gemini (which retrieve live pages), but ChatGPT and Claude draw mostly on training data plus real-time web tools. A page sitting on page three of Google can still get cited heavily by an LLM if it's structured clearly and linked from trusted sources [1].

The research backs the split. A 2024 Seer Interactive analysis found that roughly 62 percent of ChatGPT-cited URLs did not appear in the top-10 Google results for the same query [8]. That gap is where LLM SEO lives.

Before you evaluate any firm, get the lay of the land on how AI search works and why it changes the visibility math.

What should I actually look for in an LLM SEO company?

The field is young enough that almost any digital agency now claims LLM SEO capabilities. Most of them mean they added a ChatGPT section to their deliverables deck. Here are the criteria that separate real practitioners from opportunists.

Measurable AI citation tracking. A legitimate firm tracks whether your brand shows up in LLM responses, more than organic rank. Ask them to show you a live report. Real tools in this space include Profound, Goodie AI, and Perplexity's own API. If a firm can't show you a citation share metric over time, they're guessing.

Content structuring over content volume. The 2023 Princeton and Georgia Tech GEO study found that adding statistics, quotations from authoritative sources, and fluent writing to content increased AI citation rates by up to 40 percent [2]. The best firms design content around extractable facts, clear headings, and direct answers. Not word count.

Technical schema and structured data work. Gemini and Google AI Mode lean on structured data more than ChatGPT does. A firm that ignores schema markup, FAQ schema, and speakable schema is leaving Gemini visibility on the table [9].

Backlink and co-citation strategy aimed at AI training sources. LLMs weight content that appears on sources they were trained to trust: Wikipedia, major news outlets, Reddit (heavily weighted in GPT-4 training), government sites, and peer-reviewed papers. A firm should have a plan for getting your brand mentioned on those domains, more than any DA-50-plus blog.

Honest KPI setting. Nobody has clean attribution data for AI-driven revenue yet. The best firms admit this and propose proxy metrics: citation share, share of voice in LLM responses, branded search lift, and direct traffic patterns. Be suspicious of anyone promising guaranteed AI rankings. LLMs don't have stable rank positions the way Google does.

For a sharper look at what's actually measurable, see the breakdown of AI search visibility metrics and KPIs.

Which companies are leading in LLM SEO right now?

No single firm has a ten-year head start the way some traditional SEO agencies do. The landscape breaks into a few categories.

Specialist AI visibility agencies founded or pivoted specifically for GEO/AEO: firms like Goodie AI, NP Digital's AI division, and a handful of boutiques that came out of content strategy backgrounds. These tend to have the most rigorous citation-tracking methodology and the most current knowledge of how retrieval-augmented generation (RAG) systems behave.

Large integrated agencies with a GEO practice: Seer Interactive, iPullRank, and Conductor have published the most credible original research on AI search behavior. Their size means more resources, and also more chance your account gets handed to a junior team once the contract is signed.

Traditional SEO firms with an LLM add-on: this is most of the market. They're reframing existing content and link-building work as "AI optimization." That's not worthless (good SEO fundamentals do help LLM visibility), but it isn't a purpose-built AI visibility practice.

SaaS-first platforms with agency services: tools like Profound, Otterly.ai, and Semrush's AI visibility beta have started offering managed services alongside their software. The upside is deep tooling. The downside is that managed services are often a side business.

For what the tooling landscape looks like, AI SEO tools covers the major platforms.

The firm that's right for you depends more on your category, budget, and internal team than on any industry-wide ranking. A B2B SaaS company with a technical content team needs a different partner than a CPG brand starting from zero content infrastructure.

Content tactics that increase LLM citation rate

| | | |---|---| | Added statistics with attribution | 40% | | Added authoritative quotes | 37% | | Improved fluency and directness | 17% | | Added keyword density | 5% | | Added citations/references section | 30% |

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

How do I compare LLM SEO agencies before signing a contract?

Here's a comparison framework you can use in a vendor call.

| Evaluation dimension | What a strong firm looks like | What a weak firm looks like | |---|---|---| | Citation tracking | Proprietary or licensed tool, shows you a live demo | "We monitor this manually" | | Content methodology | Cites GEO research, has a documented framework | "We optimize for AI" (vague) | | Backlink strategy | Targets AI-training-weight sources (Wikipedia, Reddit, news) | Standard DA-focused guest posts | | Technical SEO | FAQ schema, speakable schema, structured data audit | None mentioned | | KPIs proposed | Citation share, share of voice, branded lift | "Top AI rankings" (not a real metric) | | Team experience | Named strategists with published work on GEO | Generalist account team | | Reporting cadence | Monthly AI visibility report with trend data | Quarterly generic traffic report | | Contract flexibility | Month-to-month or 6-month pilots | 12-month lock-in from day one |

One question cuts through a lot of noise: ask the agency which LLMs they optimize for and why. A thoughtful answer distinguishes ChatGPT (training data plus web retrieval), Gemini (live indexing, schema-heavy), Perplexity (heavily retrieval-based, cites sources explicitly), and Claude (training data plus citations when using web access). An agency that treats all four as one target doesn't understand the mechanics.

Also ask: what's your process when an LLM gives a wrong or harmful answer about our brand? Reputation defense in AI is a real and growing problem, and the best firms have a protocol for it.

What red flags should I watch for?

The LLM SEO space runs on opportunism right now. Here are the concrete red flags.

Guaranteed AI citations or rankings. LLMs don't have stable, auditable rank positions. Anyone guaranteeing "top 3 in ChatGPT" for a query is either misrepresenting how LLMs work or confusing this with Perplexity's sponsored placements (which are paid, not earned, and clearly labeled).

No mention of training data versus retrieval-augmented generation. These two pipelines need different tactics. A firm that doesn't distinguish between them is oversimplifying in a way that will hurt your results.

Keyword volume as the primary metric. Search volume from Google Keyword Planner or Semrush doesn't map to LLM query frequency. Firms still leading with keyword volume as their discovery method haven't adapted.

"We'll get you on AI Overviews" as the whole pitch. Google AI Overviews are one surface, and Google's own guidance on appearing in them overlaps heavily with standard E-E-A-T best practices [3]. That's fine, but it isn't the same as optimizing for ChatGPT or Perplexity. Conflating them is sloppy.

No original content capability. LLM visibility is built on content that contains extractable facts, attributed quotes, clear entity mentions, and structured answers. A firm that's mostly a link-building shop or a technical SEO shop with no real content production can't move the needle on the thing that matters most.

Pricing that sounds like traditional SEO retainers with an AI label. You'll see firms charging $2,000 to $5,000 per month for what is essentially a standard content retainer with a GEO slide appended. Not inherently wrong. Just know what you're buying.

How much do LLM SEO services cost?

Pricing is all over the map because the category is less than two years old. Here's the honest range as of mid-2025.

Specialist boutique firms with GEO-specific methodology typically charge $5,000 to $15,000 per month for a meaningful engagement. At the low end you're getting content audits and a structured content program. At the high end you're getting content production, citation tracking tooling, technical schema work, co-citation outreach, and reputation monitoring.

Large integrated agencies charge $10,000 to $30,000 per month for GEO as part of a broader digital program. The LLM SEO portion alone is rarely itemized clearly, which makes it hard to evaluate ROI on that workstream.

SaaS platforms with managed services run $1,500 to $5,000 per month, often bundled with the software license. This can be good value if your team has capacity to execute, because the tooling is solid and the guidance is generally sound.

Project-based engagements (AI visibility audits, one-time content restructuring) run $5,000 to $25,000 depending on site size and content depth. This is often the right entry point if you want to test a firm's thinking before committing to a retainer.

Nobody has clean public data on average contract sizes in this space yet. The numbers above come from published pricing pages and practitioner surveys from SparkToro and BrightEdge, but they're not a statistically clean sample [4][7].

Budget reality: below $3,000 per month, you're probably getting a light content program with an AI wrapper, not a genuine GEO engagement. That might be right for a small brand starting out. Just set expectations accordingly.

Does traditional SEO experience translate to LLM SEO?

Partially. And the answer matters when you're deciding whether an established agency is genuinely qualified.

What transfers: content quality fundamentals (clear writing, factual accuracy, structured headings), technical site hygiene (crawlability, page speed, clean HTML), and authority-building through legitimate backlinks. These help Google index and rank your pages, which indirectly helps LLMs that use live retrieval (Gemini, Perplexity, GPT-4 with web browsing).

What doesn't transfer cleanly: keyword strategy built on search volume, rank tracking as the primary KPI, and link-building focused on domain authority without regard for whether those sources influence LLM training. A site can have excellent traditional SEO and still be invisible in ChatGPT responses if its content isn't structured for extraction and isn't cited by the sources LLMs weight heavily.

The Princeton and Georgia Tech GEO paper made this explicit: optimizing for search engine ranking and optimizing for LLM citation are correlated but distinct objectives, and strategies that help one don't always help the other [2].

The practical implication: an agency with ten years of SEO experience has a head start on the fundamentals, but probe their AI-specific methodology rather than assuming the experience carries over. Ask them to describe a content piece they produced specifically to drive AI citation, and what happened to citation share after it went live.

For a deeper look at how AI SEO works as a discipline, that framing helps you ask better questions.

What does a real LLM SEO engagement look like month to month?

A legitimate engagement has a structured cadence. Here's what it should look like.

Month 1: Baseline and audit. The firm runs an AI visibility audit. They query 50 to 200 prompts relevant to your category across ChatGPT, Gemini, Perplexity, and Claude, and record where your brand appears, what competitors appear instead, and which sources get cited. That's the baseline. They also audit your existing content for extractability, entity coverage, and structured data gaps.

Months 2 to 4: Content restructuring and production. Based on the audit, they restructure high-priority existing pages (FAQ schema, cleaner headings, embedded statistics with citations) and produce net-new content targeting the question clusters where competitors get cited and you don't. They begin or accelerate co-citation outreach to Wikipedia, industry publications, and other high-weight sources.

Month 5 onward: Tracking and iteration. Monthly reports show citation share trends across LLMs, share of voice in target query categories, and any brand mentions in AI responses (including negative or incorrect ones that need fixing). Content production continues based on gaps revealed in tracking data.

A good firm re-runs the baseline prompt set monthly and shows you the trend line. If citation share is flat after four months of active work, that's a conversation to have.

This is where a platform like Spawned fits in: AI visibility SaaS that tracks citation share and share of voice across LLMs, giving your agency (or internal team) the measurement layer they need to show real progress instead of reporting on proxy signals.

If you want to see what citation tracking data actually looks like before committing to anything, brandrank.ai visibility insights analysis shows a real-world example of how the reporting works.

How do I evaluate LLM SEO results and hold an agency accountable?

This is the question most buyers don't ask carefully enough before signing.

The core metrics that matter:

Citation share: the percentage of relevant AI queries in which your brand is mentioned, tracked over a consistent prompt set. This is the closest analogue to keyword rank in traditional SEO, and it's what firms like Profound and Goodie AI report on.

Share of voice: among all brand mentions in AI responses for your category, what percentage are yours versus competitors? A relative metric, and more meaningful than raw citation count.

Branded search lift: if AI visibility is working, branded queries on Google should rise over time, because users who meet your brand in an AI response often search for you directly. Trackable in Google Search Console [5].

Direct and referral traffic patterns: Perplexity and some Gemini responses include clickable citations. Track referral traffic from those sources specifically [10].

Sentiment and accuracy in citations: are AI responses describing your brand correctly and positively? This needs qualitative review of actual LLM outputs, beyond binary mention/no-mention tracking.

What you should not accept as primary KPIs: organic keyword rankings (useful context, but not the point), generic traffic growth (too many confounding variables), or engagement metrics that have nothing to do with AI visibility.

Set a 90-day review gate in your contract. If citation share hasn't moved from baseline by month three, you want the right to renegotiate or exit. The honest reality is that LLM visibility can change faster than traditional SEO (because you're restructuring existing content, not waiting for new pages to rank) or slower (if the LLM's training data refresh cycle is the bottleneck). A good agency should be clear about which dynamic applies to your situation.

Should I build an in-house LLM SEO capability instead?

For some companies, yes. The agency route isn't always the right answer.

Building in-house makes sense if you publish content at high volume and velocity (the methodology needs to live inside your content team's workflow, not get applied retroactively by an agency), you have a technical SEO person who can absorb the GEO framework, and you're willing to invest in the right tooling for citation tracking.

The tooling investment is real. A credible AI visibility tracking stack runs $500 to $3,000 per month in software costs alone, depending on query volume and LLM coverage. Platforms worth evaluating include Profound, Otterly.ai, and the AI visibility tool landscape covered in our separate evaluation.

The honest trade-off: an in-house team learns your category deeply and can execute faster. An agency brings methodology developed across many clients and categories, which matters when the field is this new. Many companies end up with a hybrid: a specialist firm for strategy and methodology, and internal execution on content production.

If you're evaluating the in-house route, the generative engine optimization primer is the right starting point for building a team's foundational knowledge.

One thing to be clear about: LLM SEO is not a set-it-and-forget-it discipline. The LLMs themselves change, new retrieval patterns emerge, and competitor content shifts what gets cited. Whoever owns this function, internal or external, has to treat it as an ongoing program, not a one-time project.

What questions should I ask an LLM SEO agency in a first call?

Here are twelve questions that will tell you what you need to know. The quality of the answers matters more than the answers themselves.

1. Show me an AI visibility report from a current client (anonymized). What are the metrics, and what moved?
2. Which LLMs do you optimize for, and how does your approach differ for each?
3. What's your method for identifying which queries our brand should appear in versus competitors?
4. How do you structure content specifically for LLM extraction, and can you show me a before/after example?
5. What's your co-citation strategy? Which source types are you targeting and why?
6. How do you handle a situation where an LLM gives incorrect or harmful information about a client's brand?
7. What citation tracking tool do you use, and can I access the dashboard directly?
8. What KPIs will you be accountable to in month three? Month six?
9. How often does your team's methodology get updated as the LLMs change?
10. Who will actually work on my account day to day, and what's their background?
11. What's your minimum engagement term, and what are the exit conditions?
12. Have you worked in our specific industry? If not, what's your research process for a new category?

A firm that stumbles on questions 1, 7, and 8 in particular is not ready to deliver real results. Those three test whether they have actual measurement capability, which is the foundation of everything else.

Sources

1. Google, How Google Search Works (Crawling, Indexing, Serving)
2. Aggarwal et al., Princeton / Georgia Tech, 'GEO: Generative Engine Optimization', arXiv 2023
3. Google Search Central, Creating helpful, reliable, people-first content
4. BrightEdge, AI Search Trends Report 2024
5. Google Search Console Help, Performance reports
6. OpenAI, ChatGPT weekly active users announcement, 2024
7. SparkToro, How ChatGPT Users Search and What It Means for Marketers, 2024
8. Seer Interactive, ChatGPT vs Google: Where URLs Get Cited, 2024
9. Google Search Central, Speakable structured data documentation
10. Perplexity AI, About Perplexity