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The /universes/{id}/brands and /universes/{id}/brands/{brand} endpoints expose a brand-centric view of your dashboard data. Instead of querying ai_visibility_ranking, then ai_competitive_analysis, then geo_competitor_analysis and joining them by brand_name yourself, the API does it for you.

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What’s on a brand row

Every entry in the response carries seven numbers — three from AI mentions, two from GEO citations, plus rank and share-of-voice. Your own brand sits in the same array as every competitor.
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brand_name
string
Display name as it appears in the underlying history arrays. Preserves casing.Example: "verseodin.com", "Semrush", "tryprofound.com"
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trust_mentions
integer
Count of prompts where the AI mentioned this brand by name in its answer. Drawn from ai_visibility_ranking.Range: 0 to total_prompts for the universe.
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share_of_voice
number
This brand’s mentions as a percentage of total brand mentions in the universe. The share_of_voice values across all brands sum to ~100% (rounding aside).Example: 18.7 means this brand accounts for ~19% of brand mentions across the universe.
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visibility_rank
integer
1-indexed rank among all tracked brands by trust_mentions. 1 is the most-mentioned brand. Ties are broken by alphabetical order of brand_name.Tip: if your brand’s rank goes from 3 → 1 over a week, that’s the win you want to track.
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ai_coverage_pct
number
Percentage of prompts where the AI mentioned this brand. Drawn from ai_competitive_analysis.coverage_pct.Difference vs trust_mentions: ai_coverage_pct is the rate (mentions / total_prompts × 100); trust_mentions is the count.
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geo_coverage_pct
number
Percentage of prompts where the AI cited a URL belonging to this brand’s domain. Independent of whether the brand was named in the prose. Drawn from geo_competitor_analysis.coverage_pct.
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geo_prompt_count
integer
Count of prompts where this brand’s domain was cited at least once. Drawn from geo_competitor_analysis.prompt_count.

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Three things this view answers in one call

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”How is my own brand doing?"

KEY=vso_xxx
UNI=7f45877a-0e8e-498f-bd7f-5e70ef65a2de

curl -H "Authorization: Bearer $KEY" \
  "https://verseodin.com/api/v1/universes/$UNI/brands/verseodin.com"

{
  "data": {
    "brand_name": "verseodin.com",
    "trust_mentions": 42,
    "share_of_voice": 18.7,
    "visibility_rank": 1,
    "ai_coverage_pct": 32.5,
    "geo_coverage_pct": 12.0,
    "geo_prompt_count": 60,
    "day": "2026-04-27",
    "engine": "chatgpt"
  }
}

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"How is competitor X doing?”

Same call, just swap the brand name: 
curl -H "Authorization: Bearer $KEY" \
  "https://verseodin.com/api/v1/universes/$UNI/brands/semrush.com"

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“Show me the leaderboard”

The list endpoint sorts by visibility_rank ASC — top of the array is the most-mentioned brand: 
curl -H "Authorization: Bearer $KEY" \
  "https://verseodin.com/api/v1/universes/$UNI/brands"

{
  "data": [
    { "brand_name": "verseodin.com",   "trust_mentions": 42, "visibility_rank": 1, ... },
    { "brand_name": "semrush.com",     "trust_mentions": 25, "visibility_rank": 2, ... },
    { "brand_name": "tryprofound.com", "trust_mentions": 14, "visibility_rank": 3, ... }
  ],
  "day": "2026-04-27",
  "engine": "chatgpt"
}

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Filtering

Both endpoints accept the standard ?day= and ?engine= query params: 
# Brand snapshot for a specific past day
curl ".../brands?day=2026-04-26"

# Filter to a specific engine (when you have multi-engine data)
curl ".../brands?engine=chatgpt"
If multiple history rows match the filter (e.g. you don’t pass engine= and the universe has multiple engines), the most recent one is used. The response’s day + engine fields tell you which row was actually consulted, so you can detect this.

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When a brand isn’t found

The single-brand endpoint returns 404 not_found when:
  • The brand name doesn’t appear in any of the three source arrays for the matched history row, OR
  • No history row matches the day/engine filters
The list endpoint returns 200 with data: [] and day: null, engine: null if the filters match no history row at all.

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How this maps onto the raw history columns

If you want to do this synthesis yourself instead of using these endpoints, here are the three source columns:
Field on BrandSummarySource column on history
trust_mentions, share_of_voice, visibility_rankai_visibility_ranking[]
ai_coverage_pctai_competitive_analysis[].coverage_pct
geo_coverage_pct, geo_prompt_countgeo_competitor_analysis[].coverage_pct and .prompt_count
Match brand_name case-insensitively across the three arrays.