An AI visibility API should return the answer, not the score. That means rows at prompt, engine and run grain, the answer text itself, and cited sources as full URLs rather than domain counts. Cursor pagination and an updated_since filter decide whether a nightly warehouse sync is practical at all, and API access is usually the upsell, so compare the price of the plan that includes it rather than the entry price. The reason it is worth building: across 726 responses where a survey answer and an analytics channel both existed, 51 people named AI search and analytics recorded 2.
An AI visibility API should return the answer, not the score. If the only thing you can pull is a visibility percentage, you have bought a dashboard with an HTTP interface and you will hit its ceiling within a quarter.
That is the single most useful test when evaluating one. Everything below is the detail behind it: the endpoints that matter, the data model that survives contact with a warehouse, and the limits that decide whether a nightly sync is possible at all.
Three reasons come up, and they need different things from the same interface.
Reporting in a tool people already open. Looker Studio, a BI stack, a client dashboard. This needs aggregates, a stable schema and a connector or a clean pagination story.
Joining AI visibility to your own data. Putting citation data next to CRM records or session data, which is where AI work stops being a marketing curiosity and starts being defensible. This needs identifiers you can join on and raw rows rather than pre aggregated ones.
Feeding an agent or a workflow. Increasingly the reason people ask. This needs low latency, predictable pagination and, ideally, an MCP server rather than a REST client you have to wrap yourself.
An API built only for the first case will fail the other two, and vendors rarely say which one they built for.
Six resources cover almost every real use case. Ask for them by name.
Projects. The container. Trivial, and you need it to iterate everything else.
Prompts. The tracked questions, with their tags, markets and languages. If prompts cannot be listed and filtered through the API, automated reporting is dead on arrival.
Runs or answers. One row per prompt, per engine, per timestamp. This is the fact table. It should carry the engine, the run time, whether the brand was mentioned, its position among named brands, the sentiment, and ideally the answer text itself.
Sources or citations. One row per cited URL per answer. This is the endpoint most vendors either omit or reduce to a count, and it is the one that produces action. With it you can ask which domains win the prompts you lose. Without it you can only ask whether you lost.
Competitors. Mention rates for named rivals on the same prompts, which is what makes your own number interpretable.
Aggregates. Visibility rate, share of voice and citation rate over a period, so a dashboard does not have to recompute from raw rows on every load.
Two more are worth asking about because they signal maturity: fan out queries, the sub questions an engine generated before answering, and crawler or bot events, so you can see which AI crawlers fetched which pages.
What is the grain of the answers endpoint? One row per prompt, per engine, per run is the only grain that lets you compute anything else. Anything pre aggregated to a day or a week has thrown away information you cannot recover.
Is the answer text available? This decides whether you can do sentiment, fact checking or evidence for a correction request outside the vendor's UI. Many APIs return a sentiment label and not the text it was derived from, which means you cannot audit it.
Are cited sources full URLs or domains? Domains are cheaper to store and far less useful. You want URLs, because the page that beat you is the artefact you act on.
Are IDs stable across runs? A prompt ID that changes when you edit the prompt text will break every historical join you have.
How far back does history go? Retention is often tiered. Twelve months on an entry plan and unlimited higher up is a common pattern, and it decides whether year on year comparison is possible.
Is it read only? Being able to create and update prompts through the API is what lets an agency onboard fifty clients without fifty afternoons of clicking.
Rate limits, stated in the docs. If the limit is not documented, assume it will surprise you in production.
Cursor pagination, not offset. Offset pagination over a table that is still being written produces duplicates and gaps. Any vendor whose answers endpoint uses offsets has not thought about a warehouse sync.
Incremental fetch. An updated_since parameter, or you will re-pull the entire history nightly.
A documented, versioned schema. Ask to see the docs before buying, not after. A public API reference is itself a signal: it means the interface is a product rather than an internal endpoint someone exposed.
Sensible errors. Distinct codes for rate limiting, authentication and not found. This sounds obvious and is regularly not the case.
Be aware that in this category the API is usually the upsell rather than a feature.
The common patterns: API access gated to an enterprise tier with pricing on request; API access from a mid tier at roughly three to four times the entry price; or API and MCP included from a low mid tier. A platform charging $250 a month at entry may still put the query API behind a sales conversation, while a $189 plan elsewhere includes both API and MCP access.
So compare the price of the plan that has the API, not the entry price. Two products with similar headline prices can differ by a factor of five once the API is required, and that is the number that matters if reporting automation is the reason you are buying.
For the same reason, check whether seats are charged separately. A per seat model plus an API tier compounds quickly across a team.
The highest value thing an AI visibility API enables is not a prettier chart. It is joining answer level data to your own commercial records.
The reason is that AI referrals mostly arrive without a referrer, so analytics files them as direct traffic. In one comparison of 726 signups where both a survey answer and an analytics channel existed, 51 people said they came from AI search and analytics had recorded 2. Whatever your dashboard credits to AI, it is a floor.
An API that returns answers, sources and prompts lets you put that visibility data next to CRM records and close the gap yourself. A platform that also does the join natively, through integrations with HubSpot, Salesforce, Stripe and Shopify, saves you building it, and AI search attribution is the name for the result. Either way, the endpoint you need is the one that returns rows rather than a percentage.
updated_since filterAnything failing the first four is a dashboard with an HTTP interface. A documented AI visibility API that returns answers and sources, rather than only scores, is the difference between reporting and analysis.
The attribution comparison covers 726 post purchase survey responses where both a survey answer and an analytics channel exist for the same person, drawn from a corpus of 45,921 responses across 12 projects between 9 March and 8 September 2026. Figures are aggregated across accounts with no customer, project or private domain identifiable. That subset is small, so it sizes a direction reliably and a precise ratio less so.
Pricing patterns described here are generalisations from the published pricing pages of several vendors in September 2026 rather than a claim about any single product. Verify against the vendor's own pricing page, since tiers in this category move quickly.
What endpoints should an AI visibility API provide? At minimum: projects, prompts, answers at run grain, cited sources as URLs, competitor mentions and aggregates. Fan out queries and crawler events indicate a more mature product.
Why does the answer text matter? Because sentiment, fact checking and any evidence for a correction request all derive from it. An API returning only a sentiment label gives you no way to audit that label.
Is domain level citation data enough? No. The page that took your slot is the artefact you act on, so you want full URLs.
What should I check about rate limits?
That they are documented at all, that pagination is cursor based, and that an updated_since filter exists so a nightly sync is incremental.
Do I need an API or is an integration enough? If you only need a dashboard, a Looker Studio connector may be sufficient. If you need to join AI data to CRM or session data, you need the API or a platform that does the join for you.
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Disclosure: Finseo is an AI visibility platform and publishes an API of this kind. Pricing patterns are generalised from several vendors' published pricing pages in September 2026.
