The rise of AI search is changing what it means to be visible online. In traditional SEO, brands could often win by improving page relevance, building links, and climbing rankings for valuable keywords. But in LLM-driven environments, visibility depends on something much broader: whether a brand can be retrieved, understood, validated, and cited across a network of owned and external sources. That shift is creating a meaningful divide between traditional SEO agencies and generative engine optimization agencies.
While SEO agencies are still essential for search fundamentals, generative engine optimization (GEO) agencies are increasingly needed for brands that want to influence how they appear inside answer engines, AI search experiences, and recommendation-driven discovery.
Key Takeaways
- What is the difference between an LLM optimization agency and a traditional SEO agency? An LLM optimization agency is built to improve how a brand is retrieved, cited, compared, and framed inside AI search and answer engines, not just how individual pages rank in search results. Traditional SEO agencies typically focus on rankings, traffic, and page-level relevance, while generative engine optimization (GEO) agencies work across retrieval systems, semantic footprints, citation ecosystems, and cross-page brand representation.
- Why is LLM optimization more complex and often more expensive than traditional SEO? Because AI visibility is not a single-page problem. It requires stronger entity clarity, better semantic coverage across multiple pages, external corroboration, structured evidence, Digital PR support, and measurement systems that track prompts, citations, framing, and competitor presence. The scope is broader, more technical, and more dependent on how a brand is represented across the wider web.
- What does an LLM optimization agency actually do for a brand? A generative engine optimization (GEO) agency analyzes large-scale answer-engine data, maps semantic retrieval patterns, identifies gaps in owned and external visibility, builds cross-page narrative systems, improves citation-worthiness, and turns external footprint insights into Digital PR and authority-building strategies. The goal is to make the brand more machine-resolvable, evidence-rich, and recommendation-ready inside AI-generated answers.
7 Reasons to Hire an LLM Optimization Agency Instead of a Traditional SEO Agency
Traditional SEO still matters. But if your company wants to be visible inside AI search experiences, cited in generated answers, or recommended during high-intent research journeys, a traditional SEO agency is no longer enough.
That is because LLM optimization is not just about helping a page rank. It is about helping a brand become retrievable, understandable, comparable, and trustworthy inside systems that retrieve evidence, reason across sources, and synthesize answers. Google’s AI Mode documentation says it can break questions into subtopics and search for each one simultaneously, while OpenAI distinguishes reasoning models from standard GPT-style models for more complex tasks.
1. LLMs change the optimization problem from rankings to retrieval and reasoning
A traditional SEO agency is usually built around page rankings, click-through rates, and traffic growth. An LLM optimization agency has to think about how an AI system interprets a question, searches for evidence, and decides what information deserves inclusion in the final answer.
Why that matters:
- The model may not rely on a single page.
- The query may be decomposed into subquestions.
- Multiple sources may be compared before an answer is produced.
- Visibility depends on what gets retrieved and trusted, not just what ranks first.
This is a meaningful shift. Google’s Search documentation says AI Overviews and AI Mode may use “query fan-out,” issuing multiple related searches across subtopics and sources while generating a response.
2. LLM optimization requires understanding how modern retrieval systems work
One of the biggest gaps between SEO and LLM optimization is technical literacy around retrieval. Many traditional SEO teams are excellent at crawlability, internal linking, metadata, and content targeting.
But AI search introduces a much higher bar: teams increasingly need to understand embeddings, semantic similarity, chunking, and hybrid retrieval.
What that means in practice:
- Relevance is not only keyword matching anymore.
- Systems often evaluate semantic similarity, not just exact language overlap.
- Content may be broken into chunks and retrieved at the passage level.
- Brands need stronger semantic clarity across multiple documents, not just one URL.
OpenAI’s documentation says embeddings measure the relatedness of text strings and are used for search, clustering, and recommendations. Its file search documentation also says the system parses documents, creates embeddings, and uses both vector and keyword search to retrieve relevant content.
3. It is no longer enough to optimize a single page for relevance
This is where many SEO-first strategies start to break down.
Traditional SEO often allows for strong page-level wins. You can improve a page’s topical targeting, build links, improve internal authority flow, and gain rankings. In AI search, the surface area is larger. Models may look across product pages, documentation, FAQs, brand mentions, third-party writeups, review platforms, and other corroborating sources.
Why that raises the bar:
- Your brand needs to be legible across a wider web footprint.
- Inconsistency across sources becomes more damaging.
- Weak external corroboration can limit recommendation inclusion.
- Single-page optimization rarely solves a broader representation problem.
The better framing is that LLM optimization is a corpus problem, not just a page problem. Google’s AI search guidance explicitly points site owners toward how AI features use web content, and OpenAI’s retrieval documentation reflects how modern systems work across chunked, indexed content rather than simply pulling one page as a unit.
4. LLM visibility demands a 360-degree semantic footprint
A traditional SEO agency can often focus heavily on owned properties. An LLM optimization agency has to measure both owned and external semantic footprints and understand how they interact.
That broader footprint includes:
- Your website and content architecture
- Structured data and brand entity clarity
- Third-party mentions and corroboration
- Comparative articles and category pages
- Reviews, citations, and external expertise signals
- How competitors are framed across the same landscape
This matters because AI systems do not only ask, “What page is relevant?” They may also ask, implicitly, “What brand appears consistently associated with this topic, and what evidence supports that association?” That is a very different optimization challenge than classic keyword targeting. The technical foundation for that shift is visible in how AI retrieval systems use vector search and hybrid retrieval to identify semantically related information across sources.
5. LLM optimization is more expensive because the work is broader and more technical
This is the uncomfortable part, but it is important to say clearly.
Competing in AI search can be more expensive than competing in traditional SEO because the work often requires more inputs, more systems thinking, and more cross-source consistency. It is less about tweaking one page and more about improving how your brand is represented across an ecosystem of evidence.
That usually means investment in:
- Better content architecture
- Stronger entity and topic modeling
- More rigorous structured data
- External corroboration and digital PR
- Semantic content expansion across the funnel
- Measurement systems for citations, inclusion, and framing
So yes, the cost can be higher. But that is largely because the scope of optimization has widened. The underlying retrieval methods used across modern AI systems reflect that added complexity, especially where vector search and hybrid retrieval are involved.
6. The upside is greater because AI search can influence evaluation and recommendation, not just discovery
The reason this work matters is not simply that AI search is new. It matters because AI interfaces are moving closer to the moments where users evaluate options, compare vendors, and narrow down decisions.
That creates bigger upside because AI search can shape:
- Which brands enter the consideration set
- Which sources get cited during research
- How your company is framed versus competitors
- Whether your offering is described accurately
- Whether your brand is positioned as a recommended option
And there is evidence the channel is becoming commercially important. McKinsey reported in October 2025 that half of consumers use AI-powered search and that it could influence $750 billion in revenue by 2028.
7. The best LLM optimization agencies do not just optimize pages, they optimize representation
This is the clearest dividing line.
A traditional SEO agency may be excellent at increasing rankings, sessions, and organic click volume. But an LLM optimization agency should be able to do something broader: improve how a brand is retrieved, understood, validated, and framed inside answer engines.
That means working across:
- Entity clarity
- Semantic consistency
- Retrieval eligibility
- External corroboration
- Citation likelihood
- Comparative positioning
- Business measurement tied to AI visibility
In other words, the goal is not simply to make your content visible. The goal is to make your brand machine-resolvable and evidence-rich enough that AI systems can confidently include it in the answers that matter.
What an LLM Optimization Agency Actually Does for a Brand
A traditional SEO agency is usually hired to improve rankings, organic traffic, and page-level performance. A generative engine optimization agency is hired to improve something broader: how a brand is retrieved, cited, compared, and framed inside AI systems.
That means the work expands beyond keyword targeting and on-page edits. A generative engine optimization (GEO) agency has to study how answer engines interpret a category, what sources they trust, which pages they cite, what semantic patterns drive inclusion, and how a brand is represented relative to competitors across both owned and external sources.
Platforms like Profound are built around that exact problem, positioning themselves as tools to track AI visibility, citations, prompts, and competitive presence across engines like ChatGPT, Perplexity, Gemini, Claude, and Google AI experiences.
1. A Generative Engine Optimization (GEO) agency processes large-scale LLM execution data to understand how answer engines see a category
This is one of the clearest operational differences.
A traditional SEO agency might start with rankings, keywords, backlinks, and page audits. A GEO agency starts by analyzing prompt-level execution data across AI engines to understand how the category is actually being retrieved and synthesized.
Profound says it analyzes large volumes of real conversations and front-end AI visibility data across major answer engines, including prompts, citations, mentions, and competitive visibility.
What that work looks like:
- Group prompts by intent cluster, such as “best payroll software,” “how payroll compliance works,” “Rippling alternatives,” or “how to choose HRIS software.”
- Measure which domains appear most often by cluster, engine, and answer type.
- Separate mention frequency from citation frequency so the brand knows whether it is merely being named or actually being used as evidence.
- Analyze how the model frames the topic: definitions, comparisons, recommendations, objections, implementation steps, pricing, or trust signals.
- Identify which content assets are most often cited and which important intent clusters produce no owned citations at all.
A precise example:
- A B2B SaaS brand exports 50,000 answer-engine executions from Profound across ChatGPT, Perplexity, and Google AI experiences.
- The GEO team clusters the prompts into themes like category education, vendor comparison, implementation, ROI, and enterprise trust.
- They find the brand appears often in “what is” and “how does it work” prompts, but rarely in “best tools” or “alternatives” prompts.
- They also find that competitor mentions are strongest in prompts involving integrations, enterprise readiness, and migration risk.
- The strategic takeaway is not just “write more content.” It is that the brand has semantic strength in educational territory, but weak retrieval and weak evidence in commercial-evaluation territory.
That is the kind of diagnosis a GEO agency is hired to make.
2. A Generative Engine Optimization (GEO) agency turns prompt data into semantic maps of how retrieval works in a vertical
This is where the work becomes more analytical.
Modern retrieval systems increasingly rely on semantic similarity, embeddings, and hybrid retrieval rather than exact-match keyword matching alone. OpenAI’s documentation describes embeddings as vector representations that preserve meaning, while OpenAI and Microsoft both document retrieval systems that use semantic and hybrid search. Google’s AI search documentation also says AI features may use query fan-out, issuing multiple related searches across subtopics and sources.
That means a GEO agency has to ask:
- What semantic neighborhoods does the brand already own?
- What adjacent topics are answer engines associating with competitors?
- Which source types dominate certain prompt classes?
- Where is the brand’s narrative weak, fragmented, or absent?
A precise example:
- In fintech, the team may find that prompts around “best business bank account” consistently retrieve language tied to fees, APY, FDIC insurance, international wires, startup friendliness, and cash management.
- Even if a client has a strong landing page for “business bank account,” the answer-engine data may show that competitors are being cited because they have stronger semantic coverage across adjacent evidence themes like treasury workflows, security claims, customer support, and founder-stage positioning.
- The GEO team then builds a semantic gap map showing not only missing keywords, but missing conceptual support layers that influence retrieval and recommendation.
That is a more advanced model than a normal content gap analysis. It is closer to semantic market mapping.
3. A Generative Engine Optimization (GEO) agency designs cross-page narrative executions, not just isolated page optimizations
One of the most important ideas in generative engine optiimization (GEO )is that answer engines often evaluate a brand through a network of corroborating pages, not a single URL. Google says AI features can identify multiple supporting pages across subtopics, and OpenAI’s retrieval materials describe systems that parse, chunk, and retrieve relevant passages rather than treating a document as a single indivisible unit.
That creates the need for cross-page narrative execution.
What that means:
- The homepage states the core category claim.
- Product pages operationalize the claim.
- Comparison pages defend the claim against alternatives.
- Documentation pages provide implementation proof.
- Thought leadership pages frame the category in the brand’s language.
- Customer stories validate the claim with evidence.
- FAQ and support content reduce ambiguity around objections.
A precise example:
Imagine a cybersecurity company wants to own the narrative that it is the best choice for mid-market cloud threat detection.
A GEO agency would not simply optimize one landing page. It might execute this narrative across multiple page types:
- A category page defining the company’s position in cloud threat detection.
- An “enterprise vs. mid-market” thought leadership page explaining where incumbent tools are overbuilt.
- Comparison pages against major alternatives.
- A methodology page explaining detection logic, alerting, and integrations.
- Customer stories segmented by vertical.
- FAQ content addressing implementation time, SOC workflows, pricing model, and analyst burden.
- A glossary or learning hub that reinforces the surrounding semantic territory.
The point is to make the same narrative legible from multiple retrieval angles. When an answer engine fans out across subtopics, the brand’s position remains coherent rather than being supported by one fragile page.
4. A Generative Engine Optimization (GEO) agency measures external semantic footprint, not just owned performance
This is another major operational divide between SEO and generative engine optiimization (GEO).
Traditional SEO agencies usually measure backlinks, referring domains, rankings, and traffic. A GEO agency has to measure the brand’s external semantic footprint: where the brand appears off-site, how it is described, which source types cite it, what associations are repeated, and whether those external signals support or weaken answer-engine inclusion.
Profound’s materials emphasize citation analysis, domain categorization, competitive visibility, and understanding which prompts and sources matter to a brand. Its recent research also highlights how citation patterns differ across AI platforms and how overlap across citation pools can inform strategy.
What gets measured:
- Which external domains are most often cited for category prompts.
- Which competitors dominate those citation pools.
- Which source categories matter most: media, communities, review sites, documentation sites, LinkedIn, research publishers, industry blogs.
- Whether the brand is associated with the right attributes off-site.
- Which third-party pages are being cited when the brand is absent.
A precise example:
- A payroll software company finds that for “best payroll software for startups” prompts, answer engines frequently cite G2, NerdWallet, Zapier, startup blogs, and LinkedIn posts from operators.
- The client has strong owned content but weak off-site presence in exactly those citation pools.
- The GEO agency concludes that the brand does not have an owned-content problem alone. It has an external evidence deficit.
That diagnosis then feeds directly into Digital PR.
5. A Generative Engine Optimization (GEO) agency feeds external semantic gaps into Digital PR strategy
This is where generative engine optimization (GEO) becomes materially different from classic link building.
A traditional link-building program may focus on domain authority, anchor text, and referral value. A GEO-informed Digital PR program starts with a different question: which external sources are most likely to influence answer-engine retrieval, citation, and framing in this category?
Profound’s research and product positioning both point toward identifying which domains, prompt classes, and citation pools shape visibility across answer engines. It has also published research showing that citation behavior differs by platform and that some source types carry outsized influence in certain query classes.
A GEO-informed Digital PR workflow might look like this:
- Pull answer-engine citation data for the target prompt set.
- Rank external domains by citation frequency, cross-engine overlap, and commercial relevance.
- Classify them into buckets like editorial media, review sites, industry communities, creator platforms, analysts, or first-person expert publishing.
- Identify where competitors are repeatedly cited but the brand is absent.
- Build campaigns specifically to earn mention, authorship, quotes, contributed content, reviews, or inclusion within those source classes.
A precise example:
- For B2B SaaS prompts, the agency finds LinkedIn is heavily cited in professional query classes, which aligns with Profound’s recent research saying LinkedIn is the most-cited domain for professional queries in AI search.
- Instead of treating LinkedIn as a social side channel, the GEO team turns it into a strategic publishing surface.
- They build a subject-matter-author program for executives, product leaders, and practitioners.
- Each post series is tied to prompt clusters where the brand lacks authority.
- The goal is not vanity engagement. The goal is to improve external semantic evidence in surfaces answer engines already trust for those query types.
That is much closer to citation engineering than conventional PR.
6. A Generative Engine Optimization (GEO) agency studies citation overlap and platform-specific source behavior
Another piece of the work is understanding that not all answer engines source the web the same way. Profound’s published research says major platforms show meaningfully different citation patterns, and its volatility research argues that source selection can drift substantially over time.
That matters because a brand may look strong in one engine and weak in another.
What the GEO team does:
- Compare citation pools across ChatGPT, Perplexity, Google AI experiences, Gemini, and others.
- Look for overlap domains that repeatedly appear across multiple engines.
- Prioritize those overlap domains for content, PR, and partnership strategy.
- Track where platform-specific strategies are required.
A precise example:
- The team finds that one competitor dominates Perplexity in comparison prompts because of strong review-site coverage, while another competitor dominates ChatGPT because of broad educational content and stronger third-party expert mentions.
- Instead of one generic content roadmap, the agency builds two parallel motions:
- strengthen broad educational authority for one engine pattern
- strengthen commercial-comparison and review presence for the other
This is a more mature strategy than simply “publish more content.”
7. A Generative Engine Optimization (GEO) agency operationalizes content around evidence density and citation-worthiness
Another real category of work is content design.
Profound has written that content likely to be cited tends to be factually accurate, structured, current, and authoritative, and it has launched scoring and optimization products aimed at predicting citation likelihood based on large-scale citation patterns.
That means GEO content work often focuses on:
- stronger factual density
- clearer claim support
- better passage structure
- modular Q&A coverage
- cleaner definitions and comparisons
- fresher supporting evidence
- more explicit sourceable statements
A precise example:
A traditional SEO rewrite might add keywords, improve headers, and expand topical coverage.
A GEO rewrite may instead:
- break long blocks into retrievable passages
- add concise definitional paragraphs
- make comparative claims more explicit
- insert factual support near key claims
- build quote-worthy summaries
- add specific implementation details that help a chunk stand alone when retrieved
That is not just “better content.” It is content engineered for retrieval and citation.
8. A Generative Engine Optimization (GEO) agency builds measurement systems around retrieval presence, citations, and framing
This is where the work becomes accountable.
A generative engine optiimization (GEO) agency should not stop at “we improved your content.” It should be able to measure whether that changed the brand’s AI visibility footprint. Profound’s current positioning emphasizes prompt-level visibility, competitor tracking, citation analysis, and filtering by engine, prompt, and brand context.
A proper measurement model includes:
- prompt coverage
- brand mention rate
- citation rate
- cited URL distribution
- competitor co-occurrence
- sentiment or framing patterns where possible
- source-type mix
- engine-level visibility differences
- downstream business outcomes
A precise example:
The agency reports that over 90 days:
- prompt coverage increased from 18% to 31%
- owned citation share increased in implementation-related prompts
- competitor co-occurrence dropped in a core category cluster
- LinkedIn and third-party editorial mentions rose in citation overlap pools
- branded organic traffic did not change dramatically, but demo requests from AI-referral sessions improved
That is the kind of reporting model that makes generative engine optiimization (GEO) a channel, not just a theory.
