How to Get Your Clients Recommended by ChatGPT

The question clients are starting to ask agencies

They are going to say that they searched for their product in ChatGPT and did not see their company show up. They will want to know why a competitor was recommended instead of them.

The agency that can answer those questions with evidence will immediately look more strategic than the agency that says AI search is still too early to worry about. Clients do not need someone to hype up another acronym. They need someone who can show them where they stand, why they are missing, and what needs to change.

AI search has changed how customers discover products and the companies that are early to this will win.

AI search works differently…

Traditional SEO is built around ranked results. You have to rank on the first page in Google to win. A potential customer types a search query, scans the links, opens a few pages, and decides what to trust for themselves. In that world, the goal is to rank high enough to earn the click. Or be strategic with ads.

AI search works differently because the same customer asks a question, and the AI tool summarizes the market, names the brands, compares the options, and sometimes gives a direct recommendation. There might be 5 brands mentioned and there might only be 1-2.

That changes the entire problem.

This is why agencies need to think beyond simple prompt tracking manually. It’s essential to be consistently present in the recommendations that matter to their business.

For a SaaS company, that might include questions about the best tools in a category, alternatives to a known competitor, software for a specific team size, or platforms that solve a specific operational problem. For a local service business, it might include questions about the best provider in a city, the safest option for a procedure, or the most trusted company for a specific type of job. For an agency client, it might include questions about who to hire, what service model to choose, or which company has the most relevant expertise.

These recommendation opportunities are already happening and most clients have no reporting around them.

The agency opportunity is much larger than a simple visibility report

Many agencies will make the same mistake with AI visibility that they made with early SEO reporting. They will reduce the opportunity to a dashboard, a score, or a list of mentions. And that won’t create a valuable offer to clients.

Clients do not want a report that simply tells them they were mentioned in four out of twenty prompts. That is static. Agencies need to know if the right buyers are seeing their clients. They need to know what sources show up the most and what to publish or update next.

The best agencies will be able to audit a client’s AI presence, explain the competitive landscape, identify recommendation gaps, and build a monthly plan to improve how the client appears across AI search.

The best starting point is an AI recommendation audit

An AI recommendation audit is a review of how a client appears when potential buyers ask AI tools for advice, comparisons, and recommendations.

The goal is not to run a handful of vanity prompts. The goal is to understand how the client truly is represented in the questions that influence buying decisions.

A strong audit should show:

• Where the client appears in AI answers
• Where competitors appear instead
• How the client is being described
• Which sources are cited in the answers
• Which personas change the answer
• What gaps are preventing the client from being recommended more often

This gives the agency a much stronger deliverable. It also creates the demand for an ongoing service because every gap in the audit can become a project, content recommendation, source opportunity, or positioning improvement.

The first version of this audit does not need to be complicated. It needs to be useful. Clients should come away understanding whether AI systems see them as a serious option in their category and what needs to happen for that to improve.

Agencies need to start with buyer questions

The most common mistake in AI visibility work is starting with branded prompts.

A client will ask ChatGPT about their own company, see what comes back, and assume that is their AI visibility. But the real opportunity is in questions that don’t include your brand’s name and problem-aware questions. Agencies should be building prompt sets around the way buyers actually make decisions.

A buyer will usually start by describing a problem, asking for options, comparing familiar names, or looking for the best fit for their situation. The brands that show up in those moments have a better chance of entering the buyer’s consideration set before the buyer ever visits a website.

That is why the first step in an AI recommendation audit is prompt strategy.

The most valuable insight is the recommendation gap

A recommendation gap is the reason a competitor is recommended instead of your client. This is the concept clients will understand quickly. They do not need a technical explanation of retrieval, citations, or language models before they can see the business problem. If a buyer asks ChatGPT for the best solution in a category and three competitors appear while the client is missing, there is a gap that needs to be explained.

Sometimes the competitor has clearer category positioning. Sometimes the competitor appears in more third-party lists. Sometimes the competitor has comparison pages that directly match buyer questions. Sometimes the client’s website does not have content for the use case the buyer is asking about. Sometimes Reddit threads, review sites, analyst content, or partner pages are shaping the answer in ways the client has never monitored. And so on and so forth!

Your agency should be able to say that the client is missing from agency-related prompts because their website does not have an agency use-case page, their category positioning is too broad, and the sources AI tools cite are mostly third-party comparison articles where the client is not included.

It also makes the next step obvious. The client does not need a vague recommendation to create more content. The client needs a use-case page, comparison content, source outreach, clearer positioning, and a tracking system to measure whether those changes affect future AI answers.

The sources behind the answer matter as much as the answer

AI answers are created by the content, entities, citations, reviews, discussions, and pages that AI systems can access and interpret. And that means agencies need to study the actual sources.

If the same third-party list appears again and again, that page may matter. If Reddit threads influence the answer, community presence may matter. If competitors are repeatedly cited because of comparison pages, the client may need better comparison content or stronger inclusion in existing third-party resources. If the AI answer uses outdated positioning, the client may need to update owned content and reinforce the correct language across the web.

This is where AI visibility becomes more than prompt tracking.

The source map can become one of the most useful parts of the client report. It shows where the client is already present, where competitors have an advantage, and which external sources may need attention. It can also connect AI visibility work to PR, partnerships, content distribution, review strategy, community participation and more.

Many AI recommendation gaps will not be fixed by publishing one more blog post on the client’s own website. In some categories, the client needs to be mentioned in the places AI systems already trust.

Persona testing makes the audit feel more strategic

One of the biggest weaknesses in basic AI visibility tracking is that it treats every prompt as if the same buyer is asking. Real buyers simply do not ask questions the same way. And those differences really matter because AI answers often change based on the context inside the prompt.

An agency that tests multiple personas can uncover gaps that a basic prompt audit would miss. The client might be recommended for small teams but missing for enterprise buyers. The client might appear in broad category prompts but disappear when the prompt mentions agencies.

Instead of telling the client that visibility is low overall, the agency can explain which customer segments are seeing the client, which segments are not, and what content or authority signals are needed to improve those specific recommendation moments.

A strong AI recommendation audit becomes a client action plan

Once your agency understands the gaps, the next step is turning those gaps into specific work to do!

• If the client is missing from category prompts, build stronger category pages and third-party source presence.
• If the client is losing comparison prompts, create alternative pages, comparison pages, and more direct positioning.
• If the client is absent from persona-specific prompts, publish use-case pages for those buyer segments.
• If the client is misunderstood, update homepage copy, product pages, metadata, FAQs, and external profiles.

This is also where Reddit, review sites, customer stories, partner pages, listicles, and PR can become part of the strategy.

The service should be packaged around recurring movement

A one-time AI recommendation audit is a good entry offer, but the bigger opportunity is ongoing tracking and improvement.

AI answers change with competitors making changes, third party sources updating, new Reddit threads, models shifting and even search behavior changing. Your client that appears today can disappear next month, and a competitor that was invisible can start showing up after a big content push!

A monthly service can track the client’s most important prompts, monitor competitor movement, identify new source opportunities, report on recommendation gains and losses, and maintain a prioritized action plan. This gives the agency a natural reason to stay involved because the client can see changes over time.

AI visibility tools should support the service, not replace the strategy

There are now plenty of AI visibility tools for marketing agencies, and the category is going to keep growing. Some platforms focus on enterprise reporting, some focus on SEO teams, some focus on monitoring, and some focus on helping agencies turn scans into client deliverables.

The wrong tool will produce data that still requires the agency to do all the interpretation manually. The right tool will make the agency faster, sharper, and more consistent across clients.

Kelsey is built for agencies that want to understand why AI recommends competitors instead of their clients. It helps teams audit AI search visibility, track important prompts and topics, compare competitor presence, test customer personas, identify missed mentions, and turn those findings into clearer action plans.

That distinction matters because agencies do not only need to know whether a client appeared. They need to know why the client was missing, who appeared instead, and what work should happen next.

The agencies that move early will have an advantage

AI search is still early enough that most clients do not have a mature strategy, but it is already important enough that they are starting to ask questions.

The agencies that move early can define the process for clients before the category becomes crowded. By the time the industry has defined the category, the bigger agencies will already have case studies, workflows and proof.

But… Not by pretending that AI search has replaced SEO. Not by promising guaranteed ChatGPT rankings or by selling fear. The better move is to help clients understand a real shift in buyer behavior and give them a practical way to respond. That is a service agencies can sell.

The final takeaway for agencies

Getting clients recommended by ChatGPT is not about gaming one answer or chasing one prompt. It is about understanding how AI systems describe a market, which brands they trust, which sources they rely on, and which recommendation moments matter most to the client’s buyers.

The agencies that succeed will not be the ones that simply add “GEO” to their services page. They will be the ones that build a clear process around AI recommendation visibility. They will find the buyer questions. They will run the audits. They will identify the competitor gaps. They will study the sources. They will build the action plans. They will track movement over time.

Most importantly, they will help clients answer the question that is already becoming impossible to ignore… When a buyer asks ChatGPT who to trust, does your client get recommended, or does someone else?