AI Answer Citation & Share-of-Voice Intelligence

Use this Actor to analyze ai answer citation and share-of-voice and return decision-ready structured signals. It is designed for repeatable human, API, Apify AI, and MCP-driven workflows.

Measure brand and competitor visibility across AI-answer snapshots, track citation wins and losses, and turn mention/citation drift into decision-ready actions.

Why use this Actor

Teams need recurring evidence of which brands and domains win AI answers, which citations are gained or lost, and where competitors are taking visibility. This Actor sits above raw extraction: supply a current dataset, optionally add a previous snapshot, and receive an aggregated report built for recurring monitoring and AI-agent workflows.

Key features

• Combines brand mentions, citations and competitor share-of-voice in one snapshot model.

• Compares current and previous AI-answer evidence to surface gained and lost citations.

• Ranks the strongest visibility shifts and emits agent-ready actions.

• Reads inline JSON rows or Apify Dataset IDs with limited READ permission.

• Writes one auditable report to the default Dataset and INTELLIGENCE_REPORT.

Input

Provide currentItems directly or select an Apify Dataset with currentDatasetId. For change intelligence, add the prior period with previousItems or previousDatasetId. maxItems caps dataset loading. Optional Gen2 fields can provide a previous analysis and user-supplied economic assumptions.

Output

The Actor writes one decision-ready report to the default Dataset and to INTELLIGENCE_REPORT in the key-value store. The report includes counts, ranked signals, baseline evidence, confidence, regression state, an executive decision, recommended action, and the domain-specific portfolio score.

Example

Use the prefilled example or replace currentItems with rows from an upstream Actor. On recurring runs, provide the prior period in previousItems or previousDatasetId. The Actor normalizes common aliases, compares snapshots, ranks the strongest entity changes and emits agentAction.

Use cases

• AI search visibility monitoring.
• GEO/AEO reporting.
• brand-versus-competitor citation tracking.
• agency client reporting.

Pricing

One primary pay-per-event outcome: one decision-ready intelligence report. Base price USD 0.012 before Apify tier discounts. The 256 MB data-first architecture is designed for strong unit economics.

Limitations

• Analyzes supplied public or appropriately licensed data and does not bypass restricted sources.
• Scores are decision-support signals, not predictions or guarantees.
• Keep stable identifiers across snapshots for best change detection.
• Competitor evidence is refreshed before publication because the Store changes continuously.

Workflow

upstream dataset -> current snapshot -> optional previous snapshot -> normalization -> entity aggregation -> change scoring -> ranked signals -> agentAction.