Ai visibility local
Local only

Report

Run: 1bc20d10 · Project: metabase1-assessment · Brand: Metabase1
Generated: 2026-02-13T10:55:10.957470Z · Status: succeeded

AI Visibility Score (Buyer Discovery)

Measures how often your brand appears in non-branded, category-level AI answers, before a user knows you exist.
Score (0–100)
14.1
Formula: 0.5*brand_rate + 0.3*(100-displacement_rate) + 0.2*avg_strength, clamped 0..100
Primary score uses unbranded (buyer discovery) only. Unbranded fetched: 40 · Branded fetched: 0
What this means
Higher is better: you are present more often, displaced less often, and when you are present your mention is stronger.

Brand-Aware AI Performance

Measures how AI systems describe and position your brand when users search for it directly.
Branded performance does not affect the primary visibility score.
Score (0–100)
0.0
Formula: 0.5*brand_rate + 0.3*(100-displacement_rate) + 0.2*avg_strength, clamped 0..100
What this means
This reflects brand-aware queries only. Use it to validate positioning on branded evaluation questions.

Kpis

In neutral mode, headline KPIs use run-item customer rows (denominator: 40).
This report separates buyer-discovery (unbranded) from brand-aware (branded) queries. Primary score uses unbranded only.
Metric Value
Answers fetched (total)40
Unbranded answers fetched (primary)40
Branded answers fetched (secondary)0
Customer KPI rows (unbranded only)40
Brand mention rate (customer entity only) 2.5%
Brand absent rate (customer entity only) 97.5%
Brand missing answers39
Displaced when brand missing (of missing) 23 (59.0%)
Displacement rate (customer entity only) 57.5%
Displaced when brand missing 23 (57.5%)
Displaced despite brand mentioned 0 (0.0%)
Average strength score (customer entity only) 0.5
Mentions with competitors57.5%
Evidence coverage2.5%
Errors0

Coverage summary

MetricValue
Expected items40
Fetched items40 (100.0%)
Analyzed items40 (100.0%)
Successful items40
Failed items0
Partial completionno

Error summary

Total errors: 0 · Error rate: 0.0%
No run-item errors recorded.

Run cost estimate

Scope Input tokens Output tokens Total tokens Estimated cost (USD)
Total 2872 14056 16928 $0.030197
openai 1458 2940 4398 $0.001983
gemini 1414 11116 12530 $0.028214

By provider

Provider Answers fetched Brand rate Displaced rate Avg strength Mentions with competitors
gemini 20 0.0% 55.0% 0.0 55.0%
openai 20 5.0% 60.0% 1.0 60.0%

Your brand scorecard

Customer entities only (this should typically be just your brand).
Entity Answers Brand rate Displaced rate Avg strength
Metabase1 40 2.5% 57.5% 0.5

Competitors scorecard

Competitor entities only.
No competitor entities found for this run.

Competitive pressure

Top competitors overall
CompetitorCount
looker19
tableau18
power bi18
Top competitors when brand missing
CompetitorCount
looker19
tableau18
power bi18

Risk highlights (Buyer Discovery)

Top displaced

#ProviderEntityCompetitorsStrengthQuestionOpen
1 gemini Metabase1 tableau, power bi 0.0 What are some integrations available for Metabase? View
2 gemini Metabase1 tableau, power bi 0.0 What are some top business intelligence tools for data visualization? View
2 openai Metabase1 tableau, power bi 0.0 What are some top business intelligence tools for data visualization? View
3 gemini Metabase1 tableau, power bi 0.0 Can you list BI tools that are alternatives to Metabase? View
3 openai Metabase1 tableau, power bi 0.0 Can you list BI tools that are alternatives to Metabase? View
4 gemini Metabase1 tableau, power bi 0.0 Can you list some BI tools that excel in data integration? View
4 openai Metabase1 tableau, power bi 0.0 Can you list some BI tools that excel in data integration? View
5 openai Metabase1 looker 0.0 What are some comparable tools to dbt in the analytics space? View
7 gemini Metabase1 looker 0.0 How does Looker ensure data security compliance? View
7 openai Metabase1 looker 0.0 How does Looker ensure data security compliance? View

Top low strength mentions

#ProviderEntityStrengthQuestionOpen
1 openai Metabase1 20.0 What are some integrations available for Metabase? View

Risk highlights (Brand-Aware)

Top displaced

No displacement detected for branded queries.

Top low strength mentions

No low-strength mentions detected for branded queries.

Recommendations (Buyer Discovery)

These are generated from the same fields you already store (brand_mentioned, displacement, strength_score, evidence_snippet, competitor mentions).
  • Reduce displacement on evaluation queries
    Displacement rate is 57.5%
    Create or strengthen comparison and 'best X' pages with concise reusable proof points (pricing signals, integrations, outcomes).
    Examples:
    • What are some integrations available for Metabase?
    • What are some top business intelligence tools for data visualization?
    • What are some top business intelligence tools for data visualization?
  • Increase strength of brand mentions
    Average strength score is 0.5
    Make differentiators explicit and consistent across core pages (who it is for, what it replaces, quantified outcomes, integrations, implementation time).
    Examples:
    • What are some integrations available for Metabase?
  • Improve evidence coverage
    Evidence coverage is 2.5%
    Add verifiable snippets models can cite: customer stats, benchmark claims with sources, short feature definitions, and mini FAQs on money pages.

Recommendations (Brand-Aware)

No brand-aware recommendations yet.