Quick Answer
AEO Goal measures AI visibility by running tracked buyer prompts across configured AI answer engines, detecting whether the target brand and competitors appear, and calculating share of model from brand mentions divided by total tracked answers. The methodology also records market context, cited sources, sentiment, position, confidence ranges, and prior-period movement.
Platform Coverage
Platform coverage is the set of AI answer engines AEO Goal can query, but live coverage is always narrower than implemented coverage because it depends on configured credentials. AEO Goal includes crawler adapters for ChatGPT, Claude, Perplexity, Gemini, Google AI Overview, Microsoft Copilot, Grok, Meta AI, and DeepSeek. Those adapters are code-supported platform paths; public reporting should still distinguish implemented coverage from live configured coverage.
Some provider paths require active API keys or enabled infrastructure. When a provider key is missing, placeholder, or a non-real crawler path is disabled, that platform is skipped or excluded from persisted customer-facing math. This prevents empty placeholder responses from being counted as real AI visibility data.
Share Of Model Formula
Share of model is the percentage of tracked AI answers that mention the target brand, calculated as brand mentions divided by total tracked answers. It is the core AI visibility metric, and the AI visibility tracking product reports it alongside the supporting fields below:
| Metric | Calculation |
|---|---|
| Brand mentions | Count of tracked answers where the brand was detected. |
| Total queries | Count of tracked answers in the selected window after exclusions. |
| Share of model | (brand mentions / total queries) * 100. |
| Weighted share of model | Quality-weighted average where non-mentions contribute zero and mentioned answers are weighted by citation position and sentiment. |
Reports can also include a 95% Wilson confidence interval and a delta against the immediately prior equal-length time window. This helps teams avoid overreacting to isolated prompt swings.
Brand And Competitor Detection
Brand and competitor detection determines whether the tracked brand and named competitors appear in each answer using case-insensitive text matching. The detection layer records whether the brand appeared, how many mentions were found, and where the brand appeared in list or sentence position. Competitor overlap is captured against the competitor names attached to the tracked workflow, which feeds competitor AI visibility comparisons and a brand’s share of mind.
This is deliberately transparent. It is not a promise of full NLP entity resolution, trademark disambiguation, or every possible brand alias unless those aliases are supplied through the tracked setup.
Market And Locale Handling
AI visibility can be segmented by locale because tracked prompts carry market context that is persisted with every result row. Tracked prompts can run against market context. Market settings can include national, state, or city granularity, plus country and language context. The crawl task applies market context to the prompt and persists market fields with the resulting citation rows so reporting can segment visibility by locale.
Plan limits control how many markets a customer can configure. The pricing page lists those market limits so the marketing copy maps to enforced backend limits.
Crawl Cadence
AI visibility is measured on a schedule, not in real time: an hourly dispatcher checks which tracked prompts are due based on each prompt’s own frequency. AEO Goal uses an hourly dispatcher for due tracked prompts. Each prompt has its own check frequency in hours. Supported cadences include hourly, daily, weekly, and monthly-style schedules through the stored check_frequency_hours value. Paid recurring workflows are dispatched through the scheduled refresh path; trial and onboarding flows are handled separately.
Failures are isolated at the prompt level. A failed prompt is backed off by at least one hour so one provider or prompt failure does not block the rest of the user’s queue.
Data Included In Reporting
AI visibility reporting can include:
| Field | Purpose |
|---|---|
| AI platform | The answer engine or crawler path that produced the answer. |
| Model version | The model or crawler version label recorded with the response. |
| Brand mentioned | Whether the tracked brand appeared in the answer. |
| Competitors mentioned | Which configured competitors appeared in the answer. |
| Citation position | Where the brand appeared in the answer, when detected. |
| Sentiment and quality score | Signals used to weight answer quality. |
| Market fields | Locale context for market-level reporting. |
| Source URLs | URLs cited or extracted from the answer, when available. |
Limitations
AEO Goal does not guarantee rankings, citations, or AI answer inclusion. Third-party AI and search platforms control their own responses. Coverage depends on configured provider credentials, crawler availability, prompt design, market settings, and platform behavior at measurement time.
Google AI Overview measurement should be treated carefully: real Playwright-based Google AI Overview rows are countable, while synthetic local fallback rows are excluded from customer-facing share-of-model and citation-performance calculations.
Enterprise Recommendation
Enterprise teams should treat AI visibility as a trend measurement, not a single-answer verdict. Use stable prompt sets, documented markets, repeatable cadence, configured-platform disclosure, verified citations, and confidence intervals before making strategy or procurement decisions. Read the AI citation tracking methodology for how cited sources are verified, and how AEO differs from traditional SEO to frame these metrics for stakeholders used to keyword rankings.