Best AI Visibility API for Agencies Building Their Own Client Reporting

Good AI visibility data has three traits. It covers more than one answer engine. It returns answers with citations, not raw HTML you have to parse. And its cost tracks prompts and brands, not seats. The trap for agencies is choosing a scraping toolkit that looks cheap per request, then discovering that turning pages into brand mentions is another project entirely. Or choosing a dashboard product and finding the client data locked inside someone else’s interface. We compared five options with one reader in mind: an agency that can wire an API, Make or Google Sheets and wants its own reports across many client brands. Criteria below: platform coverage, structured output, integrations, and pricing model.

How We Built the Shortlist

We started from a longer pool of tools that touch AI search data, then cut hard. A candidate stayed only if its documentation showed support for several answer engines, output that arrives structured rather than as rendered pages, and a pricing model an agency could reason about at the volume of many clients. Anything that only sold a finished dashboard was dropped, because this article is about data you build reporting on.

The review rests on documentation, endpoint lists, integration pages and published pricing models, not on a head-to-head experiment. Where free tiers or sample requests exist, a light look at the response shape informed the notes. We also read user reviews on Trustpilot and G2, watching for patterns around support, onboarding friction and reliability rather than single scores.

The assessment relies on public information. Details change, so check current coverage before committing a client portfolio to any source.

What Separates Reporting-Ready Data From Raw Output

Agency reporting breaks in predictable places. A tool that returns rendered HTML forces the agency to write extraction logic and keep it alive whenever an interface changes. A tool that covers a single engine leaves a gap the moment a client asks about Gemini or Perplexity.

Look for citations in the response, not just answer text. A citation is what lets an agency show a client which of its pages an engine actually leaned on.

Pricing shape matters as much as the sticker. Seat-based or client-count pricing grows with the roster. Usage-based pricing grows with prompts and brands tracked, which is the number an agency actually controls. Ask what the same account can also do for ordinary SEO reporting, since two data vendors double the vendor management.

The List

1. DataForSEO

DataForSEO’s AI Optimization API is built as a data layer, not a dashboard. Four products sit inside it: LLM Mentions reports which brands get named and which sources get cited in ChatGPT and Google AI Overviews, LLM Scraper captures what ChatGPT and Gemini actually show users in their interfaces, LLM Responses sends custom prompts to ChatGPT, Claude, Gemini and Perplexity through a single interface, and AI Keyword Data supplies AI search volume with a 12-month trend.

Output arrives raw and structured, so the agency renders it under its own brand. Connectors cover Google Sheets, n8n, Make, Zapier and Airtable, and an MCP server lets Claude and ChatGPT query the same data.

DataForSEO works as an AI visibility API for agencies tracking many client brands without per-seat or per-client billing. Usage is paid for as it happens, with no monthly subscription, so the bill moves with prompts and brands tracked. One account also returns SERP, keyword and backlink data, which keeps classic SEO reporting under the same vendor. Trustpilot shows 4.5/5 from 69 reviews. Four named products, one set of reports.

Ideal for: agencies, growth leads and solopreneurs with a technical streak who can wire APIs or Make scenarios and want one data source behind their reports.

2. Oxylabs

Oxylabs comes from the web data infrastructure side. Its strength is large-scale collection: proxy networks and scraping APIs built for teams that run heavy extraction workloads.

That distinction matters for agencies. Collection power is useful when you want control over exactly which prompts and locations you query. It also means the step from collected responses to “which client brand was mentioned, and cited from where” is work your team does.

The company sits in the premium tier with a subscription model, which suits organizations already running data pipelines and budgeting for infrastructure. A lean agency testing AI reporting for a handful of clients may find the footprint larger than the need.

Ideal for: agencies with in-house data engineers who want infrastructure-level control over collection.

3. Bright Data

A large public data platform, Bright Data offers scrapers, proxies and ready datasets that teams adapt to many collection jobs. The breadth is the draw. One vendor covers a wide range of web data needs beyond search.

On G2 the company scores 4.7/5 from 337 reviews, a useful signal for an infrastructure vendor where support and reliability drive the decision.

Breadth cuts both ways for an agency focused on AI visibility. The platform is not organized around brand mentions and citations as a first-class output, so shaping responses into a client-ready schema stays on your side. Teams that already use it for other scraping can extend it. Teams starting fresh will spend setup time.

It sits in the premium tier with subscription pricing, so budget conversations differ from usage-metered options.

Ideal for: agencies already running Bright Data for other collection work and adding AI answer data to the same stack.

4. Sellm

Sellm is a mid-range, subscription-priced option aimed at tracking how brands appear in AI-generated answers. The positioning is closer to a monitoring product than to raw infrastructure, which shortens the path from signup to a first view of brand visibility.

For agencies, the question is how far you can take the output into your own reporting. A monitoring-first product is quick to start, but you should confirm how much structured data you can export and pipe into Looker Studio, Sheets or a client portal before building a workflow around it.

Subscription pricing at a mid-range tier is easier to forecast than infrastructure contracts, though agencies should test how the plan scales as client count and prompt volume grow.

Ideal for: smaller agencies that want a quicker start on AI mention monitoring and can accept a more guided product.

5. MentionsAPI

MentionsAPI targets the mentions use case directly: an API for detecting where brands show up in AI answers. A narrow focus keeps the surface simple, which appeals to teams that want a specific capability rather than a platform.

Mid-range, subscription-based pricing places it alongside the other focused options. Agencies should check which answer engines are covered, whether citations come back with each mention, and how connectors for Sheets, n8n or Make compare with the rest of the shortlist.

A narrow tool does one job and leaves the surrounding reporting stack to you. If your SEO reporting already lives elsewhere, that is fine. If you want AI visibility, SERP and backlink data under one account, you will be adding vendors.

Ideal for: agencies that want a single-purpose mentions API and already have the rest of their reporting stack in place.

Choosing for Your Agency’s Situation

If you already operate scraping infrastructure and want maximum control over collection, Oxylabs and Bright Data fit that profile, with the shaping work in-house. If you want a faster, more guided start and can live with a monitoring-style product, look at Sellm. If you want a narrow mentions API and have the rest of your reporting stack settled, MentionsAPI fits that brief. If one data feed behind many client reports matters most, DataForSEO fits that brief, with usage-based cost and wider SEO data on the same account.

Write down your client count, prompts per brand and refresh cadence before you request access to anything. The tool that stays predictable at that volume is the one worth wiring into your reports.

Frequently Asked Questions

How does pricing work for an AI visibility API for agencies?

Models differ. Some APIs bill by usage, so cost follows prompts and brands tracked. Others charge subscriptions that scale with plan tier. Agencies should model cost at their real client and prompt volume, and check whether seats or client accounts raise the bill.

What should an AI visibility API for agencies return?

Look for structured output with answer text, cited sources and brand mentions, not rendered HTML. Coverage across several engines, such as ChatGPT, Gemini, Claude and Perplexity, lets one data source support every client report without gaps.

Is an AI visibility API for agencies better than a ready-made dashboard?

An API suits agencies that want reports under their own brand inside tools like Looker Studio or Google Sheets. A dashboard is faster to start but locks data inside someone else’s interface. Teams without integration skills may prefer a dashboard.