An AI LLM SEO Agency Approach

PrimaryPosition.com: An AI-First LLM SEO Agency

Most “LLM SEO” pitches are lipstick on the same old checklists. PrimaryPosition.com takes the opposite approach: it treats AI and LLM search as a structural change in how users’ questions are decomposed, retrieved, and answered—then rebuilds SEO strategy around that reality.

From Classic SEO to AI-First Search

Primary Position started life as a search-first agency and grew up in the era of blue links, Panda, Penguin, and HCU-style quality updates. That background matters, because the team isn’t guessing how AI search works in a vacuum. They understand that LLMs sit on top of retrieval systems that still look a lot like Google and Bing, with all the bias and mess that implies.

Instead of selling “AI magic,” they focus on:

• How questions actually get asked in 2026: chat, answer boxes, AI overviews, and voice.

• How those questions get exploded into many machine-generated queries behind the scenes.

• How brands can own those expanded demand surfaces in a measurable way.

The result is an agency that still speaks SEO—but with an AI-native accent.

The Truth About “LLM SEO”

Primary Position’s core stance is simple: there is no separate “LLM ranking algorithm” you can optimize for in isolation. LLMs answer using some mix of:

• Model memory (training data and weights).

• Live retrieval from search engines and APIs.

• Private or product-specific data streams.

You don’t get to control the first bucket. The third belongs to platforms. So the agency focuses where brands actually have leverage: the live retrieval layer and the web footprint those systems can’t ignore.

That means no chasing:

• LLMS.txt “protocols” that promise direct model control but have no adoption.

• Thin rewrites “for AI” that ignore search demand and user intent.

• Rituals around E‑E‑A‑T checklists that LLMs do not literally use as ranking signals.

Instead, they work on the boring, hard things that actually move visibility: better retrieval coverage, stronger entity presence, and content that’s easy to quote.

Query Fan-Out: The New Battleground

A central idea in Primary Position’s AI playbook is “query fan-out.” When a user types one natural-language question into an AI box, the system doesn’t fire one clean keyword at a search index. It explodes that question into a cluster of related queries and evidence requests.

In practice, that means:

• Ranking for one trophy keyword is no longer enough.

• Brands need to show up across the whole cluster of “how to”, “best for”, “X vs Y”, “alternatives to” and problem-shaped queries that the system fans out into.

• LLM visibility problems are often just retrieval coverage problems in disguise.

PrimaryPosition.com builds strategies around these clusters rather than single phrases. That changes everything from how they do keyword research to how they structure content, comparison pages, and support material.

What Primary Position Actually Does as an LLM SEO Agency

Rather than bolting “AI” onto existing services, Primary Position rebuilds the stack with LLM search in mind. Typical work includes:

• Mapping AI demand surfaces

  • Identifying where AI answer boxes, overviews, and chat products are heavily used in a given vertical.

  • Reverse-engineering the likely fan-out queries behind those experiences.

• Designing content for retrieval and quoting

  • Building pages that explicitly answer the kinds of comparison, evaluation, and “what should I use?” questions AI tools need to handle.

  • Structuring content so that key claims and definitions are easy to lift and summarize.

• Expanding entity and brand presence

  • Getting brands mentioned (not just linked) in the places AI systems lean on most: strong blogs, docs, communities, and platforms that reliably rank.

  • Closing gaps where competitors are present in third-party content and you are not.

• Measuring AI / LLM visibility

  • Tracking branded presence in AI answer UIs over time.

  • Correlating changes in classic SEO (rankings, click-through, content launches) with changes in mention/citation rates in AI responses.

The output isn’t “we added FAQ schema.” It’s “your brand now shows up in the kinds of answers your buyers see when they ask AI tools for help.”