How accurate is SE Ranking AI visibility data?

AI Results Tracker runs the prompts configured in a project and stores daily answers, mentions, links, positions, and cached copies. SE Visible uses a separate analysis system and says it collects real AI responses rather than simulations or API shortcuts.

The legal terms add an important boundary: SE Visible operates through OpenRouter, which intermediates third-party AI services. The same terms say outputs can be incomplete, outdated, inaccurate, or unavailable. Buyers should preserve both statements rather than treating the product-page wording as an independent accuracy audit.

SE Visible produces three responses for each prompt and gives each response equal weight. Within a response, a brand mention receives an adaptive weight based on its best position among all brands named. A missing mention contributes zero, so frequency and position both affect the final score.

Citations do not enter that Visibility score, and sentiment has its own calculation. A buyer should therefore read visibility, source presence, citation share, and sentiment as separate signals rather than assuming one headline score contains all four.

AI Results Tracker is the change-monitoring surface and publishes daily tracking. SE Visible is the slower strategic brand view and updates weekly. SE Ranking's larger AI visibility research database publishes a monthly refresh.

That difference matters during a pilot. A daily tracker can show answer variation quickly, while a weekly or monthly product should be judged across its stated update cycle. Ask which screen, export, and API endpoint each stakeholder will use before defining freshness requirements.

Current documentation shows daily timelines, date-range filters, cached answers, exports, and historical research trends. It does not publish one universal earliest date, a fixed retention period for AI responses, or a promise that newly added prompts receive earlier answer history.

For a migration or regulated reporting program, require the earliest available date by module, retention after cancellation, deletion behavior, prompt-level backfill, export completeness, and what happens when an underlying model becomes unavailable.