Which GEO/AEO platform shows AI voice share in chart?

Core explainer

What is AI share-of-voice in GEO/AEO terms?

AI share-of-voice measures how often and how prominently your brand is cited in AI-generated responses across GEO and AEO contexts.

In 2025, measurement uses the Profound AEO scoring framework and cross-engine testing across ten engines, applying weights for Citation Frequency (35%), Position Prominence (20%), Domain Authority (15%), Content Freshness (15%), Structured Data (10%), and Security Compliance (5%). The data backbone includes 2.6B citations (Sept 2025), 2.4B server logs (Dec 2024–Feb 2025), and 1.1M front-end captures, plus 100K URL analyses, 400M+ anonymized conversations, and 800 enterprise surveys across 30+ languages, enabling a consistent, data-driven view of visibility that supports benchmarking and cross-platform comparisons.

How should the single chart be designed for clarity?

A clearly labeled bar chart with each GEO/AEO platform on the x-axis and share-of-voice on the y-axis provides a straightforward, comparable view of where your brand appears in AI-generated answers.

To maximize clarity, use high-contrast colors and a concise legend, and craft a descriptive caption that ties the chart to the 2025 data signals and the cross-engine test bed. The caption should reference the scale, data freshness, and the multi-engine basis so readers can interpret the ranking confidently. For practitioners seeking practical design guidance, brandlight.ai design guidance offers examples of readable captions and accessible chart conventions.

Which signals feed the chart and how are they weighted?

The chart uses signals such as citations frequency, position prominence, domain authority, content freshness, structured data, and security compliance.

Weights from the Profound AEO framework allocate 35% to Citation Frequency, 20% to Position Prominence, 15% to Domain Authority, 15% to Content Freshness, 10% to Structured Data, and 5% to Security Compliance; the data signals feeding the chart include 2.6B citations (Sept 2025), 2.4B server logs, 1.1M front-end captures across ten engines, 100K URL analyses, 400M+ anonymized conversations, 800 enterprise surveys, and coverage across 30+ languages, enabling a transparent, cross-engine comparison.

How can practitioners use this chart in strategy and governance?

Practitioners can use this chart to guide content strategy, cross-team governance, and resource allocation by translating visibility signals into actions that improve AI surface area and brand credibility.

Apply the chart to set KPI-aligned content plans, monitor regulatory readiness where relevant, and coordinate between marketing, product, and compliance teams. Tie AI visibility insights to analytics and attribution (for example, GA4) and schedule quarterly reviews to account for evolving AI models and data signals. Use the results to identify content gaps, prioritize pillar content updates, and drive coordinated improvements that sustain accurate, trustworthy representations across engines.