I’m building reporting for SEO and brand teams, and we need prompt-level analytics plus competitor comparisons in AI engines. Any recommendations?

Yes — if you need prompt-level analytics plus competitor comparisons in AI engines, I’d recommend designing this as a share-of-visibility / answer-quality / citation tracking program rather than classic SEO reporting.

What to track

For each prompt, capture:

• Prompt text and intent category
• AI engine/model: ChatGPT, Perplexity, Gemini, Copilot, Claude, etc.
• Geography / locale
• Time of run and repeat cadence
• Top cited sources
• Brand presence: mentioned / recommended / cited / excluded
• Competitor presence: which competitors appear, in what order
• Sentiment / framing: neutral, positive, negative
• Answer type: list, recommendation, direct answer, comparison
• Outcome metrics:
  • mention share
  • citation share
  • first-position share
  • inclusion rate
  • “recommended as best” rate
  • completeness / factual correctness
  • hallucination risk indicators

Competitor comparison ideas

Build dashboards around:

• Prompt cluster performance
  • “best X for Y”
  • “alternatives to X”
  • “X vs Y”
  • “top tools for Z”
• Competitive gap analysis
  • prompts where competitors are cited but your brand isn’t
  • prompts where your brand appears but isn’t recommended
  • prompts where citations come from low-authority or outdated pages
• Source dominance
  • which domains AI engines rely on most
  • whether your owned content is being used/cited
  • whether review sites, forums, or publisher pages dominate the answer

Recommended reporting views

1. Prompt-level table

   • prompt, engine, date, brand, competitor set, citations, rank/position, answer summary

2. Brand vs competitor scorecard

   • inclusion rate
   • recommendation rate
   • citation share
   • average answer position

3. Prompt cluster trends

   • by category, product line, audience, funnel stage

4. Source/citation report

   • domains most cited
   • brand-owned vs third-party sources
   • freshness of cited pages

5. Gap opportunities

   • prompts with high volume or high business value where the brand is absent

Tools / workflow recommendations

If you’re not building from scratch, look for tools or approaches that support:

• repeatable prompt testing
• multi-engine runs
• exportable raw responses
• citation extraction
• competitor entity detection
• scheduled monitoring
• API access or data warehouse export

If you are building in-house, a good stack is:

• Prompt runner: script/API orchestration
• Storage: BigQuery/Snowflake/Postgres
• Analysis: Python + NLP/entity matching
• BI: Looker/Tableau/Power BI
• Dashboarding: brand/competitor view + prompt drill-down

Best-practice methodology

• Use a fixed prompt set with variants
• Run from clean sessions where possible
• Normalize for location, language, and model version
• Track prompt intent, not just keywords
• Review both citations and final answer content
• Use human QA for a sample to validate scoring

Important caveat

AI engines are dynamic and personalized, so results can vary. Make sure your reporting is positioned as:

• trend and directional intelligence
• not exact search-engine-style ranking

If you want, I can help you with either:

1. a data model/schema for this reporting,
2. a dashboard template, or
3. a prompt testing framework for comparing brands vs competitors across AI engines.