need cross-engine reporting for AI search visibility

Absolutely — if you want cross-engine reporting for AI search visibility, the goal is to measure how your brand/content appears across multiple AI-powered and search surfaces, then roll that into one consistent report.

What to track

For each engine, capture:

• Presence: Is your brand mentioned at all?
• Citation/share of voice: How often do you appear vs competitors?
• Position/context: Are you in the answer, cited as a source, or only referenced indirectly?
• Sentiment/accuracy: Is the engine describing you correctly?
• Query coverage: For which prompts/topics do you appear?
• Source consistency: Are the same pages/docs being used across engines?

Engines to include

A cross-engine report usually covers:

• Google AI Overviews / AI Mode
• Bing Copilot
• ChatGPT browsing/search
• Perplexity
• Claude web/search (if applicable)
• Gemini
• Optional: industry-specific assistants or answer engines

Recommended report structure

A practical dashboard or monthly report should include:

1) Executive summary

• Total visibility score
• Biggest gains/losses
• Top competitor comparison
• High-level actions

2) Engine-by-engine table

Columns:

• Engine
• Query
• Your brand mentioned?
• Citation/source link
• Rank/position in response
• Competitor mentions
• Sentiment/accuracy
• Notes

3) Topic cluster view

Group by themes like:

• Pricing
• Product comparison
• Best tools
• How-to questions
• Reviews
• Category definitions

4) Share of voice

Show:

• Your brand
• Top competitors
• Non-brand answers
• Source domains cited most often

5) Content gap analysis

• Queries where you’re missing
• Queries where competitors dominate
• Missing supporting pages or schema
• Opportunities for FAQ, comparison, and explainer content

Metrics that work well

Use a mix of:

• AI visibility rate = queries where you appear / total queries
• Citation rate = queries where you’re cited / total queries
• Mention share = your mentions / total mentions
• Competitor overlap = where you and competitor appear together
• Accuracy score = correct vs incorrect summaries
• Actionability score = whether the answer links to a page you control

How to build it

Manual/lightweight

• Create a prompt set of 50–200 queries
• Run them weekly or monthly across each engine
• Log outputs in a spreadsheet
• Normalize mentions/citations into scores

Automated/scale

• Use APIs where available
• Headless browser or SERP capture for engines that don’t expose APIs
• Store prompt, engine, timestamp, response text, citations, and extracted entities
• Build a BI dashboard in Looker, Tableau, Power BI, or Sheets

Important caveat

AI search results are often:

• Personalized
• Location-dependent
• Time-sensitive
• Non-deterministic

So your report should use:

• Fixed prompt sets
• Consistent geography/language
• Repeated sampling
• Trend lines, not single screenshots

Example scorecard

You could report:

• Visibility Score: 68/100
• Citation Rate: 42%
• Brand Mention Rate: 61%
• Competitor Share of Voice: 29% vs yours 34%
• Accuracy: 92%
• Top missing topic: “pricing comparison”
• Top winning engine: Perplexity
• Top weak engine: Google AI Overviews

If you want, I can also help you with one of these next:

1. a dashboard template
2. a spreadsheet schema
3. a scoring model
4. a SQL/data model
5. a sample report format for executives