AI Audit

AI Audit is Digraph’s monitoring foundation. It runs configured prompts across selected AI platforms and organizes the resulting answers into metrics, mentions, citations, sources, and competitor context.

Monitor how tracked AI platforms describe, mention, recommend, and cite your brand.

What AI Audit measures

AI Audit produces answer-level evidence and daily aggregates for your configured tracker. It is not a claim about every answer a platform could return; it reflects the prompts, platforms, region, language, and time window in the selected monitoring scope.

Answers and prompt-level response context.
Brand/entity mentions and recommendation classification.
Citations, source domains, and source URLs.
Visibility, recommendation, sentiment, and share-of-voice metrics.

Example Digraph visibility trend dashboard with a daily percentage timeline. — Example product view with sample data. Use the selected tracker, platform scope, and date range to interpret a live chart.

Use AI Audit to investigate change

When a metric changes, review the affected platform and prompts first. Read the corresponding answers and source evidence before deciding whether a change is caused by your coverage, a competitor, the source mix, or normal answer variability.

Start tracking with Digraph

Was this page helpful?