Core Service
AI Search Reporting
The AI search era needs new metrics. Impressions and rankings do not describe what is actually happening inside generated answers.
- AI search analytics
- AI visibility reporting
- LLM analytics
- AI share of voice reporting
- AI SEO reporting
How our AI search reporting approach works
Our AI search reporting layer replaces stale SEO dashboards with AI search analytics your CMO and board will actually read. Share of voice, citation velocity, prompt-level appearance, sentiment, and pipeline attribution — the AI visibility reporting model we run for every retained client. LLM analytics is embedded, not bolted on: the same prompt universe that drives strategy drives measurement.
Share of voice
Weighted SOV across your priority prompt universe, per engine and per competitor.
Citation velocity
Rate of new citations on high-authority third-party sources, tracked as part of AI search analytics.
Pipeline influence
Attribution linking AI visibility reporting to sourced and influenced pipeline — the metric CFOs care about.
What you get
Engagement deliverables
- Executive AI search reporting dashboard
- Monthly AI visibility reporting narrative
- Quarterly LLM analytics deep-dive
- Prompt-level appearance data feed
Related AI search reporting services
Related reading
Frequently asked about AI search reporting
How is AI search reporting different from SEO reporting?
AI search reporting measures presence inside generated answers — citations, mentions, recommendations — not just rankings and clicks. Both matter; AI search analytics captures the AI-native side.
Can this integrate with our existing BI stack?
Yes. Our AI visibility reporting exports to warehouses (Snowflake, BigQuery) and dashboards (Looker, Tableau) so LLM analytics flows into your existing BI.
Ranking is no longer enough
You need to be cited, mentioned, and recommended.
Get a free AI Visibility Report — see exactly where your brand appears across ChatGPT, Google AI, Gemini, Perplexity, and Copilot, and where competitors are winning instead.