I'm building a dashboard to track branded and non-branded prompts across AI search tools, and I need it to tell me which pages are used, how often we get cited, and whether visibility changes after content updates. What stack or platform approach makes sense?

A good fit is a hybrid “AI search analytics” stack:

Recommended approach

1) Data collection layer

Use a combination of:

• Prompt set management for branded vs. non-branded queries
• Scheduled SERP / AI answer scraping or API pulls from tools like:
  • Google AI Overviews / Search Console where available
  • Bing Copilot / Bing search
  • Perplexity
  • ChatGPT browsing/search experiences if accessible via approved methods
  • Other AI search platforms you care about
• Page-level content inventory from your CMS, sitemap, or crawl tool

2) Storage layer

Put everything into a warehouse:

• BigQuery, Snowflake, or Postgres if the scale is moderate
• Tables you’ll want:
  • prompts
  • runs
  • ai_results
  • citations
  • pages
  • content_versions
  • page_metrics

This makes it easy to compare visibility before/after content changes.

3) Processing / attribution layer

Build logic to answer:

• Which URLs are cited most often
• Which prompts trigger citations
• Whether citations come from:
  • exact page
  • category page
  • blog content
  • product page
• Whether changes in content correlate with:
  • citation frequency
  • ranking/visibility
  • inclusion in AI answers

A simple approach is to create:

• canonical URL matching
• citation normalization for URL variants
• content version tagging tied to publish dates

4) Analytics / dashboard layer

Use a BI tool:

• Looker Studio for lightweight and fast setup
• Tableau / Power BI for more robust reporting
• Metabase / Apache Superset if you want open-source and SQL-friendly

Build views for:

• branded vs. non-branded prompt performance
• citation share by page
• citation trends over time
• visibility changes after updates
• prompt-level response consistency
• source-type breakdown

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Best stack depending on maturity

If you want fastest time to value

• Airtable or Notion for prompt tracking
• Python scripts + browser automation/API pulls
• BigQuery/Postgres
• Looker Studio or Metabase

If you want scalable and cleaner architecture

• Airflow / Prefect for orchestration
• Warehouse: BigQuery or Snowflake
• dbt for transformations
• BI: Tableau / Power BI / Metabase
• A content crawler like Screaming Frog, Sitebulb, or a custom crawler

If you want a productized platform

Look for tools in:

• AI search visibility / GEO / AEO tracking
• LLM answer monitoring
• Search appearance monitoring These are emerging, so verify:
• prompt-level history
• citation extraction
• change detection after content updates
• export/API support
• branded/non-branded segmentation
• page-level attribution

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What I’d recommend for your use case

For the exact needs you described, I’d build:

• Prompt library: branded/non-branded classification
• Automated runs: daily or weekly against selected AI/search tools
• Citation parser: capture cited URLs/domains and normalize them
• Content-change tracker: ingest CMS publish/update timestamps
• Warehouse: BigQuery or Postgres
• Dashboard: Metabase or Looker Studio
• Optional: dbt for transforms and Airtable for review workflows

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Core metrics to include

• Total citation count
• Unique cited pages
• Citation rate per prompt
• Branded vs. non-branded citation share
• Visibility by tool
• Visibility delta after content update
• Freshness of cited pages
• Citation concentration by page
• Prompt coverage rate

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Practical warning

The hardest part usually isn’t the dashboard—it’s reliable data capture from AI tools, since outputs can be personalized, rate-limited, or difficult to access consistently. So the platform should prioritize:

• repeatable runs
• timestamped snapshots
• normalized citations
• clear prompt/version control

If you want, I can also sketch a reference architecture diagram or recommend a stack based on your team size and budget.