What Does an LLM SEO Agency Actually Do for Your Brand?

The three lanes of LLM SEO work

The phrase "LLM SEO" gets used loosely, so it helps to separate what an agency actually does into three distinct workstreams. The first is generative engine optimization (GEO): restructuring existing pages, publishing new comparison and evaluation content, and marking it up so ChatGPT, Perplexity, Gemini and Google AI Overviews can extract and cite it accurately. The second is paid visibility inside chat interfaces — OpenAI opened a US ad pilot in February 2026 and moved to self-serve via Ads Manager in May 2026, with CPM, CPC and oCPC bidding in a relevance-weighted auction. The third is an ongoing AI presence audit: a standing measurement of how often, how accurately, and how favorably a brand shows up when real prompts are run against the major models.

Most agencies pick one lane. Suggesting.ai runs all three together because they compound: audit data tells you what to fix organically, and paid placement fills the gap while the organic work matures. Treating them as separate projects, run by separate vendors on separate timelines, usually means the paid spend and the organic content never learn from each other — the audit that should inform both instead sits in a slide deck nobody revisits.

What GEO work looks like day to day

Concretely, GEO means auditing which pages already get cited and why, then rewriting the pages that don't. Models reward content that answers a specific question directly in the first two sentences, uses tables and lists a human can scan, states numbers and dates plainly, and avoids vague adjectives. An agency will also fix technical blockers: crawler access rules for OAI-SearchBot, ChatGPT-User and PerplexityBot, schema markup, canonical tags, and page load speed on the pages that matter most for citation.

A second, less obvious piece is distribution. Models weight third-party mentions — review sites, comparison roundups, forums, industry press — alongside a brand's own site. Part of the work is earning those mentions deliberately rather than hoping they accumulate.

How to evaluate whether an agency is doing this well

Ask for the prompt list they test against, not just the pages they've edited. A serious agency runs dozens of realistic buyer prompts monthly — "best broker for X", "is Y platform regulated in Z" — and tracks whether the brand appears, in what position, and what's said about it. If an agency can't show you a before/after citation snapshot, they're guessing.

• Do they separate organic GEO reporting from paid ad reporting?
• Can they name which crawlers they've verified access for?
• Do they show competitor citation share, not just your own?

It's also worth asking how they handle a model that gets a fact about your brand wrong — a lapsed certification, an old pricing tier, a discontinued product. Correcting a factual error inside an AI answer isn't the same job as ranking a page, and an agency that's only ever done traditional SEO may not have a process for it at all.

The forex broker example

Take a regulated forex broker. A prospective trader asks ChatGPT "which broker is licensed to operate in the UAE and offers tight spreads on gold". The model needs to answer confidently, which means it needs a page somewhere — ideally the broker's own site — that states the license, regulator, spread figures and account minimums in a form it can lift cleanly. Without that page, the model defaults to whichever competitor already has one. LLM SEO agency work here is the difference between being the answer and being the also-ran a chatbot never mentions.

This is exactly the environment Suggesting.ai's finance clients — Economies.com, FxNewsToday.ae, Tawsiyat.com and others — operate in daily, where comparison-driven, high-ticket buyers ask AI before they ask a human.

What Suggesting.ai does specifically

Suggesting.ai starts every relationship with a free 48-hour audit covering current citation rate across ChatGPT, Perplexity, Gemini and Copilot, crawler access status, and a gap list against named competitors. From there, work splits into GEO content and technical fixes, managed ChatGPT ad campaigns where available in-market, and a monthly AI presence report. The goal in every engagement is the same one-liner: when an AI model is suggesting a provider in your category, Suggesting.ai exists to make AI suggest you.

Measurement and reporting

Reporting should show citation frequency by model, answer sentiment, and share of voice against two or three named competitors, refreshed monthly. Traffic from AI referrals is worth watching too — studies report it converts several times better than average Google organic traffic (one 2026 study found 15.9% versus 1.76%), so even modest referral volume can matter more than it looks like on a traffic dashboard.

A useful report also flags regressions early. Models update frequently, and a citation that existed last month can disappear after a training or retrieval change with no warning. Agencies that only report wins, never regressions, are hiding the part of the job that actually requires ongoing attention rather than a one-time content push.

Where this fits alongside existing marketing

LLM SEO work doesn't replace a content or demand-gen team — it gives that team a new distribution target. The blog posts, comparison pages and case studies a marketing team already produces are the raw material; an LLM SEO agency restructures and republishes that material so it's citation-ready, then measures whether it's actually being picked up. Companies that treat this as a bolt-on to existing content operations, rather than a separate silo, tend to see faster results because the underlying subject-matter expertise is already there.