How Agencies Track AI Search Visibility Across Client Portfolios
AI search is shaping how clients' brands get discovered — and agencies need to track it across every account. Here's how to build AI visibility monitoring into a scalable, billable service.
Filipe Lins Duarte
|March 14, 2026|9 min read|For Agencies
Clients are actively asking about AI search. It is no longer just an emerging channel. ChatGPT, Perplexity, Gemini, and Google AI Overviews now shape how buyers discover brands, compare vendors, and form opinions before they ever click a link. Agencies that cannot track that exposure and report on it are missing a channel that is growing fast.
The problem is that most AI visibility tools were built for single brands, not agency workflows. One client is manageable. A portfolio of 15 to 30 brands, each with its own competitive landscape, prompt sets, and reporting requirements, is a different problem entirely. This guide covers how agencies are solving it: the workflows, the deliverables, and what to look for in a tool that scales with a client book.
AI Peekaboo
Profound
Otterly
Peec AI
Scrunch
Starting price
Affordable
$99/mo
€29/mo
€85/mo
$250/mo
Agency/multi-client plan
Yes (all plans)
Enterprise only
Partner program (Standard+)
Separate agency pricing
Enterprise only
Unlimited seats
Yes
No (1 on Starter, 3 on Growth)
Yes
Yes
5 on Core
White-label reporting
Yes, native
Not at standard tiers
Custom Looker Studio (Partner only)
Not at standard tiers
Not at standard tiers
Free trial
Yes
Yes
Yes
Yes
7 days
The Agency Challenge: Managing AI Visibility at Scale
Tracking AI visibility for one brand is relatively straightforward. Define a set of prompts, pick the AI platforms you care about, and start monitoring.
Doing that for 20 clients is operationally intensive. Each client has a different industry, competitive set, target persona, and reporting cadence. The prompt configuration alone can take hours per client if you are starting from scratch. Then there is the question of how to present the data: a CSV in a shared folder, a custom dashboard, or a branded report? And when a client's AI visibility drops mid-month, who catches it and how fast?
These are the questions agencies are working through right now. The ones getting ahead of them are turning AI visibility into a systematized, billable service rather than a one-off audit.
5 Agency Use Cases for AI Visibility Monitoring
Use Case 1: Running a Baseline AI Visibility Audit at Client Onboarding
Before you can show progress, you need a baseline. For most agencies, the onboarding audit is now the first deliverable in any new client engagement that touches organic or AI-driven visibility.
A solid baseline covers three things: how often the client's brand appears in AI-generated answers for relevant prompts, what sentiment those answers carry, and which sources the AI engines are citing. That third point matters more than most clients realize. It tells you whether the client's own content is driving AI citations or whether they are getting cited indirectly through third-party coverage.
The audit also establishes competitive context. Running the same prompt set against two or three competitors shows the client exactly where they stand in share of voice, not just in absolute terms.
Key takeaway: Agencies that can run this audit quickly across new clients, using saved prompt templates and cloneable workspaces, turn a two-day process into a two-hour one.
Use Case 2: Monthly Competitive Benchmarking Across Client Brands
Competitive benchmarking is where AI visibility tracking becomes genuinely recurring work. The question shifts from "does our brand appear?" to "are we gaining or losing ground versus competitors, and why?"
Monthly benchmarking reports typically cover share of voice trends across tracked prompts, sentiment shifts worth flagging, new competitors entering the AI citation landscape, and which sources have started or stopped appearing alongside the brand.
For agencies running this across 10+ clients, speed is the constraint. Manually pulling data, reformatting it per client, and writing up context does not scale. The agencies doing this efficiently have standardized both the prompt configurations they run and the report structure they deliver.
Key takeaway: Standardized prompt templates and report formats are what separate agencies that can offer AI visibility as a scalable service from those doing it manually case by case.
Use Case 3: Building White-Label AI Visibility Reports for Client Deliverables
Reporting is where the agency-client relationship is most visible. A well-designed AI visibility report, delivered under the agency's brand, reinforces the value of the engagement and makes the data feel like a managed service rather than a raw export.
Most AI visibility tools do not offer native white-labeling at standard pricing tiers. What agencies end up doing instead: exporting data into custom Looker Studio templates, copying screenshots into branded slide decks, or stitching together reports manually each month. That process works, but it is friction-heavy and time-consuming. Native white-label output changes that entirely. Reports are generated directly from the platform, under the agency's brand, without a secondary production step. We have covered the broader landscape of white-label AI visibility reporting in more depth elsewhere, but the gap between "Looker Studio workaround" and "native white-label" is significant in practice.
Key takeaway: Native white-label reporting is the difference between a report that takes 20 minutes and one that takes a day. Most tools at standard pricing tiers do not have it.
Use Case 4: Flagging AI Visibility Drops as a Proactive Retention Signal
This use case is underutilized and one of the most valuable for client retention. When a client's AI visibility drops, fewer citations, rising negative sentiment, or competitors suddenly appearing on previously owned prompts, that is an early signal something has changed in how AI engines are representing the brand.
Catching that proactively and flagging it before the client notices it is exactly the kind of work that justifies a retained agency relationship. It is the difference between reporting what happened and actually managing the account.
To do this well, you need monitoring that runs frequently enough to catch drops quickly. Daily monitoring is more useful than weekly. You also need alert logic that surfaces material changes rather than normal variance.
Key takeaway: Agencies that build proactive AI visibility monitoring into their workflow are delivering a risk management service, not just a monthly report. That is a different, stickier value proposition.
Use Case 5: Pitching AI Visibility as a New Billable Service Line
The pitch is straightforward. AI search influences purchase decisions before a prospect ever visits a website, and most brands have no visibility into how they are represented in those answers. The agency can fix that.
The most effective pitches use live data. Running a quick audit on a prospect's brand before the meeting, showing them that a competitor appears in 40% of relevant AI searches while they appear in 8%, creates immediate urgency. That kind of concrete, unexpected insight opens budget conversations without requiring a lengthy explanation of why AI visibility matters.
For the service model, most agencies are packaging AI visibility as a standalone monthly retainer, a bolt-on to existing SEO retainers, or a one-time audit that converts to ongoing monitoring. All three work. The recurring monitoring model tends to have the strongest retention because the benchmark compounds in value over time.
Key takeaway: A live audit of the prospect's brand is the most compelling sales tool available. It takes 30 minutes to prepare and tends to close the conversation faster than any slide deck.
How the Main Tools Handle Agency Workflows
Profound
Profound starts at $99/month with a single seat on the Starter plan and three seats on Growth at $399/month. Multi-client management and agency-specific features require the Enterprise tier, which is custom priced. For agencies managing more than a handful of clients, the per-seat structure at standard tiers is a real constraint.
Otterly
Otterly has the most developed agency program of the tools covered here. The Partner tier is available on Standard (€189/month) and Premium (€489/month) plans, and includes extra prompts, pitch workspaces for prospect demos, unified billing across client workspaces, and custom Looker Studio reporting with agency branding. Worth noting: that Looker Studio reporting is a workaround, not native white-label output. It requires setup and ongoing maintenance per client.
Peec AI
Peec AI has a separate agency pricing page and offers unlimited users across all plans, starting at €85/month. The project-based structure (1 project on Starter, up to 5 on the Advanced plan at €425/month) maps reasonably to client-by-client billing. No native white-label at published tiers.
Scrunch
Scrunch has dedicated agency pricing at $500/month for the Agency Core plan, which includes three brand workspaces and unlimited user licenses. Full LLM coverage across nine platforms and access to AXP require the Enterprise tier. The steepest entry price in the category.
Why AI Peekaboo Fits the Agency Model
AI Peekaboo was built with agency workflows in mind. Three things that matter specifically for agencies running multiple client accounts:
Unlimited seats on every plan. No per-seat cost as your team or client-facing headcount grows. Add account managers, strategists, and client stakeholders to any workspace without touching your plan.
Native white-label reporting. Reports are generated directly from the platform under your agency's brand. No Looker Studio setup, no secondary production step, no per-client template maintenance. That is a meaningful operational difference at volume.
Affordable entry point. Most AI visibility tools price for enterprise budgets. AI Peekaboo is built to be practical for agencies at every tier, not just the ones with six-figure retainers.
AI Peekaboo tracks brand visibility across ChatGPT, Perplexity, Gemini, Google AIO, and Google AI Mode. For a broader comparison of tools across different agency and marketing team use cases, the AI monitoring tools roundup for marketing teams covers the wider landscape.
Getting Started
The fastest path is to start with one client. Run a baseline audit, build a simple monthly report template, and stress-test the workflow before rolling it out across the portfolio.
Pick a client whose competitive landscape you know well. You will contextualize the data faster, write sharper commentary, and show up to the first debrief with actual insight rather than raw numbers. Once the workflow is sharp, replicating it is straightforward.
Agencies entering this space now are building a real lead on competitors who are still treating AI visibility as optional. The clients asking about it today are the ones committing to it in 12 months.
If you want to see how AI Peekaboo's white-label reporting and multi-client setup work in practice, book a demo tailored to agency workflows.
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Filipe Lins Duarte
I'm Filipe, the CEO & Co-Founder of Peekaboo. I lead all commercial and customer facing functions here at the company. I am obsessed about making sure our customers are heard and have a great experience with us!
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