What does a ChatGPT visibility agency do, exactly?

The three things a ChatGPT visibility agency actually does

Strip away the marketing language and the work breaks into three concrete pieces: diagnosing where a brand currently stands in AI-generated answers, doing the content and technical work to improve organic citation, and managing paid placement where the platform supports it. Everything else — strategy decks, workshops, positioning language — sits on top of that core.

This is different enough from traditional SEO that treating it as "SEO with an AI label" misses most of the actual work, which is specific to how large language models retrieve and summarize information.

It's worth being specific about the word "agency" here too — some vendors sell only software that scans for AI mentions, without doing any of the content or paid-media work themselves. That's a monitoring tool, not a visibility agency, and the two get conflated often enough that it's worth clarifying in the first sales call. A monitoring dashboard can be a useful input to the work, but it isn't the work itself — someone still has to act on what it shows. When comparing proposals, ask directly whether the deliverable is a report or a set of shipped changes to your content and campaigns.

Step one: the audit

Before any optimization work starts, an agency should run a structured set of prompts — the ones your actual buyers would type — against ChatGPT, Perplexity, Gemini and Google AI Overviews, and document exactly how (or whether) your brand shows up versus named competitors.

• Which prompts currently surface your brand, and which don't?
• What sources are the models citing — your own site, review platforms, press, or nothing?
• Is your content technically accessible to citation crawlers like OAI-SearchBot and PerplexityBot?
• How does this compare to your two or three closest competitors on the same prompts?

This step should produce a written baseline, not just a verbal impression, since everything that follows gets measured against it. A good baseline also documents the exact prompt wording used, since small changes in phrasing can produce noticeably different answers from the same model. This is also why a one-time snapshot isn't enough — the same prompt should be re-tested periodically since model behavior and training data both shift over time.

Step two: generative engine optimization (GEO)

Generative engine optimization restructures existing content — comparison pages, FAQs, documentation, review responses — so an LLM can extract clear, unambiguous facts rather than having to infer them. This often means adding structured data, tightening claims to be verifiable, and making sure the same facts (pricing, licensing, features) are consistent everywhere the model might find them.

It also includes a technical layer: checking that crawlers used for AI citation are allowed in robots.txt, distinct from the training crawlers (like GPTBot or ClaudeBot) some brands choose to block for separate policy reasons.

A less obvious part of GEO is off-site work — making sure review platforms, industry directories and press coverage state the same facts your own site does, since a model often draws on several sources at once and inconsistency between them reduces citation confidence. This off-site coordination is usually the slowest part of GEO work, since it means engaging with platforms and publishers the brand doesn't fully control. Expect this part of the work to run on a longer timeline than on-site content changes, which the brand can update immediately.

Step three: paid ChatGPT Ads and what Suggesting.ai does for clients

Where OpenAI's Ads Manager is live, an agency can run paid campaigns inside ChatGPT conversations — labelled "Sponsored," shown separately from the answer, and matched to the conversation topic rather than cross-web tracking. CPM, CPC and oCPC bidding are available with no stated minimum spend, though availability is expanding market by market.

Suggesting.ai runs all three layers together for clients across finance and trading media — Economies.com, FxNewsToday.ae, ECCrypto.com and others — starting every relationship with the free audit so the GEO and paid work that follows is scoped against actual gaps, not guesswork.

Because Perplexity, Gemini and Copilot don't yet have an open self-serve ad platform, paid work today concentrates on ChatGPT specifically, while GEO work covers all engines simultaneously since it depends on content quality rather than an ad account. Expect this balance to shift as more engines open ad platforms of their own over the coming years, which is another reason to pick an agency comfortable working across engines rather than one built around a single platform's ad tools.

Worked example: what this looks like for a forex broker

A forex broker asks: what would we actually get? The audit shows whether ChatGPT already recommends the broker for prompts like "best MT5 broker in Southeast Asia," and against whom. The GEO phase then tightens the broker's regulatory disclosures and comparison content so the facts are unambiguous. If the target market has ChatGPT Ads live, a campaign runs against the highest-intent comparison prompts, measured against qualified account openings, not raw impressions.

The same three-step structure applies whether the client is a broker, a SaaS company or a consumer brand — only the specific prompts, content types and conversion event change. Understanding that structure is usually more useful for evaluating a proposal than memorizing any single industry's specific tactics, since the three-step audit, GEO, and paid-ads framework applies regardless of what the company sells.