How Astiva measures AI visibility
Every number Astiva reports is defined, computed, and refreshed on a published cadence. This page documents the full pipeline — query sampling, mention extraction, brand normalization, sentiment scoring, and freshness — so every claim is traceable to a method.
The 7 AISO Metrics
- Visibility Percentage — share of tracked prompts in which the brand is mentioned at least once. Reported 24h / 7d / 30d.
- Share of Voice — SoV = brand mentions / (brand + competitor mentions), computed over the same prompt set.
- Average Position — where the brand appears inside each AI response (first, middle, or later), weighted.
- Brand Sentiment — tone of each mention, scored positive/neutral/negative at the sentence level and aggregated.
- First Mention Rate — share of responses in which the brand is named before any competitor.
- Mention Frequency — total brand mentions per window, de-duplicated at the response level.
- Sentiment Volatility — week-over-week variance in sentiment; flags emerging perception issues early.
Pipeline: From query to dashboard
- Query Sampling — platform-specific query set across informational, commercial, and transactional intent; rotated to reduce cache bias.
- Mention Extraction — parses each response for mentions, position, sentence-level sentiment, and the competitive set.
- Brand Normalization — resolves mentions across casing, spacing, hyphenation, and misspellings; cross-validated against ground-truth human reviews.
- Sentiment Scoring — mention-level scoring aggregated per platform and per window; Sentiment Volatility exposes variance.
- Freshness Cadence — daily automated runs across every platform in the customer's plan; dashboard refreshes within 24h; alerts within 24h of visibility shifts.
What "accuracy" means at Astiva
Accuracy refers specifically to brand normalization accuracy — the rate at which the engine correctly resolves a mention to the intended brand entity. Measured against a versioned, human-reviewed evaluation set. Does not refer to sentiment classification or aggregate metric accuracy.
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