Brand Performance by AI Model
Metrics
Brand Performance by AI Model
Shows composite performance scores broken down by AI model (ChatGPT,
Gemini, Claude, etc.), revealing platform-specific brand strength variations.
This endpoint calculates the composite score separately for each AI source,
enabling identification of which platforms favor which brands. A brand might score 85 on ChatGPT but only 65 on Gemini, indicating inconsistent AI positioning. Critical for platform-specific optimization strategies and understanding where to focus brand building efforts across different LLM ecosystems.
**When to use:** User wants to compare brand performance across AI platforms, identify model-specific weaknesses, optimize for specific LLMs, analyze consistency across sources, or find platform preferences.
**Common user queries:**
- "How does Nike perform on ChatGPT vs Gemini?"
- "Compare brand scores across different AI models"
- "Which AI favors our brand?"
- "Show score variations by LLM"
- "Is brand performance consistent across platforms?"
- "Get composite scores by source"
**Returns:** Array of brands with composite scores for each AI source.
Example: [{"brand": "Nike", "source": "chatgpt-4", "composite_score": 85.5}, {"brand": "Nike",
"source": "gemini", "composite_score": 72.0}]
Required permission: `read:analysis`.
GET
Brand Performance by AI Model
Authorizations
Path Parameters
A UUID string identifying the analysis.
A UUID string identifying the organization.
A UUID string identifying the workspace.
Query Parameters
Filter by one specific brand.
Filter by a collection date being greater than or equal the specified date. YYYY-MM-DD format
Filter by a collection date being less than the specified date. YYYY-MM-DD format
Interval of the timeseries.
Available options:
month, week Response
200 - application/json
Pattern:
^-?\d{0,1}(?:\.\d{0,6})?$Pattern:
^-?\d{0,1}(?:\.\d{0,6})?$Pattern:
^-?\d{0,1}(?:\.\d{0,6})?$Pattern:
^-?\d{0,1}(?:\.\d{0,6})?$