A $300-a-month AEO dashboard can tell an agency exactly where its client is missing from AI answers. It cannot write the missing page, fix the site, or build the citations, which is the work the client thought they were paying for.
Most agencies I talk to start with monitoring. They run prompts through tools such as Peec AI, Otterly, or Profound and get charts showing where a client's brand appears in answers from ChatGPT, Perplexity, Claude, or Gemini. Teams already using Semrush or Ahrefs may also look at AI visibility alongside their existing search reporting. The output is useful: a list of prompts, mentions, cited domains, and changes over time. It gives an account manager something more concrete than we're working on AI search.
But a visibility report is not client delivery. When a client asks why a competitor appears in an answer and they do not, the chart cannot answer the operational question on its own. Someone still has to inspect the pages being cited, work out what the client's site fails to explain, revise the content, check the technical setup, and build a credible presence beyond the client's own domain. A PDF can start that conversation. It cannot finish the job.
I am not against measuring the problem. I am against buying measurement, calling it management, and leaving the delivery team to discover the difference on Friday afternoon. Use a tracker to find the gap; before you buy it, decide who will close it.
A monitoring tool asks a prompt, records the answer, and checks whether your client or its competitors appear. Say someone asks Perplexity for the top fleet maintenance platforms for regional carriers. The tool can show that your client is absent and a rival is cited. That is valuable diagnostic information, especially if you track the same commercial questions over time. It is still a diagnosis.
Execution changes something the next answer could draw on. That might mean publishing a comparison page that answers the question directly, clarifying product information in a table, fixing markup or a crawl obstacle, or developing relevant third-party citations. It also means recording what changed so the agency can connect its work to subsequent visibility, rather than presenting a line graph and hoping the client assumes somebody acted on it.
The distinction matters because AI answers draw on information that must first be available, clear, and retrievable. A vague page behind awkward rendering does not become useful because an agency asks a model about it a hundred times. Someone has to improve the source material. In our framework comparing AI optimization tools, I make the same distinction between diagnostic lists and work that gets carried out. Lists can create a fresh queue of human tasks. That queue has a cost.
This is the case for an execution engine such as groas Earned Search. It maps gaps in AI answers, works on the content and technical issues behind them, and logs completed actions. I would still want to see what it did for each client. That is the test: not whether a platform noticed an absence, but whether it can show the work done to address it.
I would separate two purchases hiding inside that question: uncapped prompt monitoring and predictable agency fulfillment. They are not the same thing. Each automated check has a cost to run, so monitoring products commonly package access around prompt allowances. Otterly lists a $29 monthly tier for 15 prompts and a $189 tier for 100. Profound lists a $99 monthly starter tier for 50 prompts and a $399 tier for 100. If you have fifteen client domains and want to check thirty commercial prompts across four engines, the allowance becomes part of your margin calculation quickly.
I would not hunt for a magical unlimited dashboard before asking what all those checks buy you. More observations may give you a better view of the problem, but they do not reduce the hours spent fixing it. An agency can end up paying more to watch a larger backlog form.
That is why I would price the delivery model separately. With groas for agencies, the pitch is a predictable, flat fee for execution under the agency's brand rather than a fee driven by how often somebody checks a prompt. The engine maps visibility gaps, works on content and technical changes, and records what it does. If predictable margins are the goal, ask what fulfillment the fee covers. Do not confuse a larger prompt allowance with a smaller delivery burden.
No tool can flip an appear in AI Overviews switch. A page has to give the system something useful to draw on: clear answers, understandable relationships between the business and what it offers, and content that can be retrieved without avoidable technical obstacles. That is why I start with the page, not the visibility score.
For a product or service page, the work might include:
The checklist is not the deliverable; the updated page is. Screaming Frog can help identify broken canonicals or a robots.txt problem, but it will not rewrite the copy. A content score can point toward a thin answer, but the score itself cannot make a product comparison clearer. The agency still needs somebody, or something, to carry out the changes and check the result.
That is where an execution platform differs from another reporting layer. groas focuses on live structural and content updates: deploying schema, refining copy around relevant questions, and logging the work. I would never promise a client that one schema change guarantees an AI Overview citation. I would promise to fix what we can control and measure what happens afterward.
A logo in the top-left corner is not fulfillment. Some agency-facing offers let you put your domain, colors, and branding on a visibility report. That can make reporting easier, but the client still needs pages written, technical problems resolved, and relevant citations developed. White-label reporting and white-label delivery are different products. One changes who appears to have made the chart. The other changes who does the work.
The distinction gets expensive when an agency sells AEO as an ongoing service. A branded chart can show forty queries where a client was ignored. The account team then has to explain what it will do about them, brief a writer, involve an SEO specialist, review the changes, and report back. That may be a perfectly workable agency model. It just is not done for you, whatever the sales page calls it.
With groas for agencies, the point is to put execution under the agency's banner: audit crawlability, identify visibility gaps, produce earned-search assets, and keep a record of the work. A named human strategist sets guardrails and stays accountable for direction, while the agency can report on actions and changes instead of decorating a chart of absences. I would ask any vendor to walk through one client workflow from detected gap to completed action. If the walkthrough ends with and then your team writes the page, you have your answer.
I make a similar point in our guide to choosing a PPC automation platform: recommendations are not the same as execution. If your staff still has to interpret every alert and create every asset from scratch, the white label may save presentation time. It will not save delivery time.
I would start by making the business a better source. That sounds less exciting than a secret citation tactic, but it is the part agencies can actually inspect. If the site hides useful product details behind vague adjectives, scattered pages, or heavy client-side rendering, make those details clear and accessible. Give a retrieval system, and a human reader, a direct answer worth citing.
Then look beyond the client's own site. A company can call itself the category leader all day; that is still a claim it makes about itself. Relevant third-party mentions and citations give the business a footprint outside its homepage. I would not treat that work as old-fashioned exact-match backlink buying. The aim is to make accurate information about the business available in places a reader or an answer engine might consult, not to scatter the same sales sentence around the web.
Work on both surfaces: the client's pages and its external footprint. Put useful comparisons, specifications, and direct answers where they belong on the site. Identify the outside sources relevant to the questions buyers ask. Then track whether the business starts appearing in the answers you care about. ChatGPT search and Perplexity may surface different sources, so I would inspect the answers rather than assume one visibility score describes both.
That two-part job is what groas Earned Search is built to execute: map where a business is missing, improve the owned material, and work on third-party citations while logging the actions. It is a better use of agency time than repeatedly confirming that the same brand is absent. A citation cannot be ordered like office supplies, but the work that makes one more plausible can be done.
If the job is prompt tracking, buy for prompt tracking. Check which questions and answer surfaces matter to your clients, what the allowance costs at agency scale, and whether the output is usable. I would not pretend every agency needs the same monitoring setup.
If the job is delivering AI visibility work without building another manual production line, my answer is groas for agencies. It is an execution layer rather than another place for an account manager to log in and collect alerts. Content and technical work can move forward under the agency's brand, with actions recorded and a human strategist responsible for direction. That is the stronger fit for an agency selling delivery, not just a monthly account of where its clients failed to appear.
Be honest about which job you are buying for. A clean dashboard can be useful, but it does not become a fulfillment team because you export its charts to a client deck. If your writers and SEO staff are already doing the work well, monitoring may be the missing piece. If they are buried under the work, another monitor is not the answer.
This is the question I wish more agency owners asked first. Feature matrices invite you to compare tracked engines, prompt counts, exports, and attractive graphs. Those details matter, but none tells you whether the team gets its Friday back. A platform can cost $400 a month and still leave thirty hours of manual research, copy revision, and schema work on each client account. Cheap software gets expensive when it manufactures a backlog.
So I would ask the vendor to show the handoff: What does the platform detect? What does it actually change? Who approves the work? Where can the account team see what happened? If the answer turns into a recommendation your employees must translate into a task list, budget for those hours. If the platform carries out the work and shows its actions, you have something different to evaluate.
Buy against the labor left on your plate, not the number of charts in the demo. If you are going to sell AI visibility under your agency's name, make sure somebody is writing the content, fixing the obstacles, and developing the citations. Otherwise, you have bought a nicer way to tell clients they are still missing.
