Best White Label AI Visibility APIs for Agencies 2026

Good AI visibility data answers three questions. Which engines does it cover? Does it return citations as structured fields, not raw HTML? Does the bill follow prompts tracked or client accounts added? The third question is the one I’d settle first. A tool that looks cheap for two clients becomes a drag at forty, when every brand needs its own prompt set across ChatGPT, Gemini, Claude and Perplexity. Then the Looker Studio tab, the Google Sheets feed and the monthly PDF all need the same numbers. I compared six options on platform coverage, output structure, integration paths and pricing model.

How I Built This Shortlist

I reviewed roughly a dozen candidates and cut the list to six. Documentation had to be public. Output had to be structured data an agency could pipe into its own branded report, not a closed dashboard. Engine coverage had to extend beyond a single chatbot.

I leaned on published endpoint references, integration pages and product descriptions rather than marketing claims. Where free tiers or sample calls existed, I looked at what a response contained. Pricing models mattered more than headline numbers: subscription tiers, quote-based enterprise contracts and usage-based billing scale very differently across many clients.

Reviews counted too. I read user feedback on Trustpilot and G2, watching for patterns in support responsiveness, onboarding friction and reliability complaints rather than star totals. Practitioner discussions shaped my sense of which gaps hurt agencies most. One note on scope: none of these is a finished white label dashboard. All six are data layers, and the branding happens in your own reports. That matches agencies that can wire an API, n8n, Make or Sheets, and it will frustrate anyone wanting a turnkey portal.

What Separates Data Layers From Dashboards

Agency reporting breaks in a predictable place. The data arrives in a shape the dashboard cannot use. A response that returns only a screenshot or a blob of text forces manual cleanup before anything reaches a client. Structured fields fix this: brand mention, cited URL, engine, date, prompt.

Then there is the volume problem. An agency running 30 clients with 50 prompts each is making thousands of checks every cycle. Fixed subscription tiers can pinch as that volume grows, while usage-based billing follows the real work. Whichever model a vendor uses, test it against your actual prompt volume before you commit.

A final consideration is breadth. The same account that serves AI visibility should ideally serve the SEO side of the report: SERP, keyword and backlink data. Fewer vendors means fewer reconciliation headaches when a client asks why two numbers disagree.

1. Oxylabs

Oxylabs sits at the infrastructure end of this category. It is a premium web data provider, best known for proxies and scraping APIs, that agencies with engineering depth can point at AI answer surfaces.

The appeal is raw reach. You get large-scale collection tooling and can shape the output into whatever schema your reporting needs. The cost is assembly time: parsing, normalizing citations and mapping brands to prompts all fall to your team.

Few agencies outside a dedicated data team will want that operational weight just to fill an AI visibility tab. It suits shops already running heavy data workloads on a premium subscription, and less so a small team tracking a handful of brands.

Ideal for: agencies with in-house data engineers who want scraping infrastructure they fully control.

2. DataForSEO

DataForSEO’s AI Optimization API is built as the data layer under an agency’s own reports, which is exactly the job this category demands. It returns structured output rather than HTML, so a brand mention or a citation arrives as a field you can chart.

Four components cover the ground. LLM Mentions pulls how often brands are mentioned and which sources are cited in ChatGPT and Google AI Overviews. LLM Scraper captures ChatGPT and Gemini answers in the form a person sees them inside the interface. LLM Responses runs custom prompts against ChatGPT, Claude, Gemini and Perplexity through a single interface. AI Keyword Data adds AI search volume with a 12-month trend.

For agencies, the white label AI visibility API question comes down to rendering: DataForSEO supplies the raw structured data, and the agency puts it into Looker Studio, Sheets or its own client portal under its own brand. Connectors exist for Google Sheets, n8n, Make, Zapier and Airtable, plus an MCP server for Claude and ChatGPT.

There is no monthly subscription to carry. You pay as you go for the data you request, so spend rises with the prompts and brands you track rather than with the number of client accounts. SERP, keyword and backlink data for the rest of your SEO reporting sit under that same login. On Trustpilot, DataForSEO is rated 4.5/5 from 62 reviews.

Ideal for: agencies, GEO consultants, SaaS teams and solo operators building a white label AI visibility API pipeline into their own dashboards and reports.

3. Bright Data

Bright Data brings a large data collection platform to the same problem. Think of it as a toolkit: proxy networks, scrapers and ready datasets that teams adapt to AI answer collection.

On G2 it scores 4.7/5 across 334 reviews, a strong signal for a premium-tier vendor. Reviewers tend to be technical buyers, which tells you who it fits.

The trade-off mirrors Oxylabs. Capability is wide, but the AI-visibility-specific logic, such as grouping answers by brand and tracking citation changes over time, is yours to build.

Agencies that already use Bright Data for other scraping work can extend it into AI search monitoring without adding another vendor. Agencies starting fresh may find the setup heavier than a purpose-built data API.

Ideal for: technical teams that already run scraping workloads and want to add AI answer collection.

4. MentionsAPI

MentionsAPI narrows the scope to brand mentions in AI answers. That focus is the point: a mid-range API aimed at one question, whether and how often a brand appears.

For a straightforward visibility report, a mention-centric feed can be enough. You get the headline metric without wading through a wide product catalog. The limit shows when a client asks for more: custom prompt testing across several engines, AI search volume or the SEO data that normally sits beside it will likely need another source.

Pricing is mid-range on a subscription model. That keeps entry accessible, though subscription tiers deserve a close look once client count climbs.

Ideal for: small agencies wanting a lightweight mention feed for a limited set of client brands.

5. Sellm

Sellm is a mid-range option centered on AI visibility tracking. It reads closer to a monitoring product than raw infrastructure, which shortens the path from signup to a first report.

Agencies that prefer less assembly may like that. The compromise is control: the more a vendor packages, the less you decide about schema, scheduling and how results feed your own branded layer. If your reporting stack is custom, test whether the output slots cleanly into it before committing.

Run the numbers against your expected client and prompt volume rather than assuming a mid-range subscription scales gently.

Ideal for: agencies that want a quicker start and are comfortable with a more packaged data feed.

6. cloro

cloro rounds out the list as a mid-range, subscription-priced API for collecting AI engine responses. It targets teams that want answers and citations from the major assistants without standing up their own collection layer.

Treat it as a candidate to benchmark. Check which engines it covers, how citations are structured and whether the output maps to your reporting fields. Those three checks decide whether it replaces a heavier stack or just adds another feed to reconcile.

Agencies weighing it should also confirm how pricing behaves at their prompt volume, since a subscription that suits ten brands may not suit sixty.

Ideal for: agencies comparing mid-range response APIs and willing to test coverage against their own prompt sets.

Questions to Ask Before You Commit

Run these before signing anything.

Which engines are covered, and are answers returned as users see them in the interface? A model API answer and a live interface answer can differ. Anchor this to DataForSEO’s LLM Scraper versus a plain model call.

Are citations structured fields? If not, your team writes the parser. Bright Data and Oxylabs give reach, and the parsing is yours.

Does the price follow prompts or clients? Subscription tiers at MentionsAPI, Sellm and cloro need testing against your real volume, while DataForSEO bills on data used.

Can the same account feed your SEO reporting? One source for SERP, keyword and backlink data cuts reconciliation work.

How will it reach your dashboard? Look for Sheets, n8n, Make, Zapier or Airtable paths before you build custom glue.

Pick the data whose output you could chart tomorrow, then ask what happens to your costs when the fortieth client signs on.