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LLM Optimization Agency: Get Your Brand Cited by ChatGPT, Claude, Gemini, and Perplexity

Search is splitting in two. One half still runs on blue links. The other half runs on a model that reads a question, decides what it already "knows," pulls a handful of sources to fill the gaps, and writes an answer, with your brand in it, or without. And Zeros is the LLM optimization agency built for that second half. We do LLM SEO: the technical, structural, and content work that makes a brand a trustworthy, citable source inside large language model answers, not just a ranked result on a search engine results page.

If your team has already invested in SEO, you have a head start, but LLM optimization is not SEO with a new coat of paint. It's a different retrieval system, different citation logic, and a different set of signals. This page covers what LLM optimization actually involves, how models choose what to cite, who needs this work now, and how And Zeros approaches it.

What LLM Optimization Is

LLM optimization (also called AEO, GEO, or LLM SEO depending on who's using the term) is the practice of structuring your brand's content, data, and public footprint so language models can find it, understand it, trust it, and cite it in response to relevant questions.

Traditional SEO optimizes for a ranking algorithm that returns a list of links. LLM optimization optimizes for a generation process that synthesizes an answer and selectively credits its sources. That changes the job in a few concrete ways:

Structure over keywords

01
Models parse content for clear claims, definitions, comparisons, and entities, not keyword density. Pages that answer a specific question directly, in plain language, get pulled into answers more often than pages built around ranking tactics.

Machine-readable trust signals

02
Schema markup, consistent entity data (your name, description, and facts about your business, stated the same way everywhere), and structured FAQ content give models an easier path to citing you correctly.

Third-party corroboration

03
Models weigh what independent sources say about you (reviews, comparisons, forums, press, directories) alongside what you say about yourself. LLM optimization includes building and monitoring that outside footprint, not just your owned site.

Retrieval-friendly technical foundations

04
Crawlability, clean HTML, fast load times, and content that isn't locked behind JavaScript rendering or paywalls all affect whether a model's retrieval layer can access your content in the first place.

Ongoing measurement

05
Because model outputs change with every update and every prompt variation, LLM optimization is a continuous practice of tracking where and how a brand shows up in AI answers, not a one-time audit.

The output isn't a ranking position. It's a citation, a mention, a recommendation: the model naming your brand as the answer, or one of the answers, to a question your buyer is asking.

How LLMs Choose What to Cite

Every major model (ChatGPT, Claude, Gemini, Perplexity) handles citation differently under the hood, but the underlying logic follows a consistent pattern:

The model decides if it needs outside information

01
For well-established facts, it answers from training data alone. For anything current, specific, local, or competitive ("best LLM optimization agency," "who does GEO for SaaS companies"), it triggers a retrieval step and pulls in external sources.

Retrieval ranks candidate sources

02
The model's retrieval system (often backed by a search index, like Bing for Copilot or Google for Gemini, or a proprietary crawler like Perplexity's) scores pages for relevance, authority, and freshness: similar inputs to traditional search ranking, but the output feeds a synthesis step instead of a results page.

The model reads for clarity and extractability

03
Pages with direct, well-scoped answers near the top, clear headings, and unambiguous claims are easier for the model to lift and paraphrase accurately. Vague, marketing-heavy copy gets skipped even if it's technically relevant.

The model weighs consistency across sources

04
If your own site, a third-party review, and an industry roundup all describe your company the same way, that consistency reinforces the claim. Contradictory or missing information elsewhere makes a model less likely to cite you confidently.

The model attributes and cites

05
Some tools show citations inline (Perplexity, Gemini, Copilot); others (ChatGPT's default mode) may reference a brand by name without a visible link. Either way, the underlying selection process is the same: retrieval, evaluation, synthesis.

The practical implication: brands that show up in AI answers usually have some combination of structured, direct content on their own site, strong entity clarity, and independent validation elsewhere on the web. LLM optimization is the discipline of building all three, deliberately.

Who This Is For

LLM optimization matters most for brands where the buying decision involves research and comparison, which is most B2B and considered-purchase B2C categories. It's a fit if:

01
Your buyers are already asking ChatGPT or Perplexity questions like "best [category] for [use case]" or "[competitor] vs [competitor]" before they ever hit Google.

02
You compete in a category where a handful of names dominate AI answers, and you're not currently one of them.

03
Your SEO program is solid but you've noticed AI-referral traffic climbing while you have no visibility into whether models mention you.

04
You're launching a new product or category and want to shape how models describe it before competitors do.

05
You run a services business, SaaS product, or agency where "who should I hire/use for X" is a query pattern models get asked constantly.

It's less urgent (though still worth watching) for purely local, transactional businesses where discovery still runs almost entirely through maps and directory search.

Why And Zeros

And Zeros is an AEO/GEO agency built specifically for this shift, led by founder Doug Saltzman. We’re not a traditional SEO shop that added “AI” to the service list. LLM optimization is the core of what we do, and our process reflects that:

We audit your current AI visibility first

01
Before we touch anything, we map how ChatGPT, Claude, Gemini, and Perplexity currently answer the questions that matter to your business, including whether you're mentioned, how accurately, and who's beating you.

We fix the structural foundation

02
Schema, entity consistency, crawlability, and content architecture: the technical layer that determines whether models can retrieve and trust your content at all.

We build citable content

03
Direct-answer pages, comparison content, and FAQ structures written the way models actually extract and paraphrase information, not written for keyword tricks that don't work in a generative answer.

We strengthen your footprint beyond your own site.

04
Third-party corroboration is part of the citation equation, and we treat it that way.

We track results the way this channel demands

05
Ongoing monitoring of AI answer visibility across models and prompts, because a single audit tells you where you stand today, not whether the work is holding.

If your brand isn't showing up when your buyers ask AI tools for a recommendation, that's a fixable problem, and it's the specific problem we solve.

Book an LLM visibility audit
Talk to And Zeros about LLM SEO

Frequently Asked Questions

01What's the difference between LLM optimization and traditional SEO?

Traditional SEO optimizes content to rank in a search results page built from links. LLM optimization optimizes content to be retrieved, understood, and cited inside a generated answer. The two share some technical foundations (crawlability, clear content, structured data) but the evaluation and output are fundamentally different: a ranking position versus a citation or mention.

02Which AI tools does LLM optimization cover?

And Zeros works across the models buyers actually use for research and recommendations: ChatGPT, Claude, Gemini, and Perplexity. Each has its own retrieval and citation behavior, so the work includes understanding how each one sources and presents answers in your category.

03How is LLM SEO different from AEO or GEO?

These terms largely describe the same practice: AEO (answer engine optimization), GEO (generative engine optimization), and LLM SEO are used somewhat interchangeably across the industry. And Zeros treats them as one discipline: getting your brand accurately and favorably represented in AI-generated answers.

04Can you guarantee my brand will be cited by ChatGPT or Perplexity?

No agency can guarantee a specific citation, because model outputs vary by prompt, update cycle, and retrieval source, and no vendor controls a model's training or ranking process. What And Zeros can do is systematically improve the structural, content, and trust signals that make citation more likely, and measure whether visibility is improving over time.

05How long does LLM optimization take to show results?

Timelines vary by category, competitive density, and starting point. Structural fixes (schema, crawlability, entity clarity) take effect as models next index your content. Content and footprint work compounds over a longer period, similar to how authority builds in traditional SEO, which is why we treat this as an ongoing program with regular measurement, not a one-time project.

05Do I still need traditional SEO if I invest in LLM optimization?

Yes. Search engines remain a major discovery channel, and strong SEO fundamentals (crawlable sites, clear content, authoritative signals) also support LLM optimization. The two programs reinforce each other; And Zeros builds LLM optimization to work alongside your existing SEO investment, not replace it.