# How to Check GEOly's AI Visibility Data: Accuracy, Evidence and Limits - Canonical: https://www.geoly.ai/blog/geoly-data-accuracy-methodology - Published: 2026-11-13 - Updated: 2026-11-13 - Author: [GEOly AI](https://www.geoly.ai/blog/author/geoly-ai) - Tags: [AI Visibility](https://www.geoly.ai/blog/tag/ai-visibility), [Measurement](https://www.geoly.ai/blog/tag/measurement), [GEOly AI](https://www.geoly.ai/blog/tag/geoly-ai) > **Summary:** An AI visibility score is useful only when you can explain its denominator and inspect the underlying answers. Here is how to evaluate GEOly data without confusing monitoring with market-wide truth. GEOly measures the AI answers collected for a project's monitored questions. Its visibility numbers describe that monitored sample under the selected platform, date and brand configuration. They do not describe every conversation on an AI platform. This is GEOly's own explanation, not an independent review or accuracy certification. A seven-day first-party query-fan-out export retrieved on November 13, 2026 included searches asking about GEOly's “data accuracy,” “reliability” and “methodology.” The right response is a reproducible verification workflow, not an unsupported accuracy percentage. ## Key Takeaways - Compare numbers only after matching the project, dates, platform and topic filters. - Check whether answers have completed and have been counted. - Open answer text and sources to verify an individual result. - Treat absent collection, pending calculation and a measured zero as different states. ## What a monitored record represents A monitored record is an answer to a tracked question on a particular platform and observation day. Two different platforms can answer the same question differently. A later run can also change its wording, ranking or sources. Country, language, prompt wording, collection time and platform are part of the context. A scheduled observation is evidence of that observed response, not a guarantee that every user sees the same result. Use [prompt tracking](https://www.geoly.ai/blog/prompt-tracking) to define a stable test set. Keep branded questions separate from non-branded discovery questions: a question that names the brand has a different opportunity to mention it. ## Match the metric to the question Brand mention rate answers whether the tracked brand appears in counted answers. A citation metric asks whether the answer cites the brand's domain under that metric's eligibility rules. A citation-source table counts source occurrences; it is not an answer-based citation percentage. Relative share also depends on the comparison set. A share against configured competitors and a share across automatically discovered brands can have different denominators. Record which population the report uses. For a project headline, use the overview returned for the selected scope. Its per-platform table lets you compare the same question across engines. Do not use an unweighted average of daily percentages as the monthly figure. The [AI visibility metrics guide](https://www.geoly.ai/blog/ai-search-visibility-metrics) explains the broader measurement vocabulary. The live response's window and metric definitions remain the authority for a particular export. ## A five-step verification procedure 1. Save the project, country, platform, date window and prompt/topic filters with the export. 2. Record the counted-answer denominator and any pending or excluded records. 3. Pick several mentioned, unmentioned, cited and uncited answers across different days. 4. Read their full text, tracked brand spellings and returned citation sources. Check whether a source is your own domain, a retailer, a review site or another company. 5. Reconcile the samples with the overview using the same counting rules. Escalate a mismatch with the record identifier and report window. This process tests an interpretation. A small spot check does not establish a universal error rate. An accuracy claim needs a defined labelled sample, reviewer protocol and disclosed results. ## Freshness, missing values and historical coverage Today's collection can be partial. Some answers can be complete while derived metrics are still computing. A platform may be included in a plan but have no observations in the selected window. Read the availability and completeness fields before interpreting a blank chart. Do not convert “not ready” into 0%. For a weekly comparison, check the number of collection days in both periods. Historical data from a retired platform should be labelled historical. In GEOly's current monitoring context, Perplexity is historical through September 20, 2026, and Copilot through October 11, 2026. Current standard coverage and custom arrangements should be checked on the [pricing page](https://www.geoly.ai/pricing). ## What the data cannot prove An AI mention is not a click, an order or a causal lift. A positive sentiment label is not a customer review. An absent citation does not prove that no source influenced the answer. Likewise, a website audit evaluates the checks in its report; it does not guarantee future recommendations. Use [GEO audits](https://www.geoly.ai/blog/geo-audit) and measured answer evidence together. For repeatable exports, see [programmatic monitoring](https://www.geoly.ai/blog/programmatic-ai-visibility-monitoring) and the [Data API documentation](https://docs.geoly.ai/developers/data-api). To assess suitability before purchasing, use the [GEOly evaluation checklist](https://www.geoly.ai/blog/geoly-evaluation-checklist). Written by the [GEOly editorial team](https://www.geoly.ai/blog/author/geoly-ai). More on [AI Visibility](https://www.geoly.ai/blog/tag/ai-visibility).