Perception

Documentation Index

Fetch the complete documentation index at: https://www.pierview.ai/docs/llms.txt

Use this file to discover all available pages before exploring further.

The Perception dashboard breaks down sentiment beyond a single score. It shows which LLMs are positive or negative about your brand, which categories drive that signal, and what specific language AI models use.

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Sentiment score

Your overall sentiment score is a 0–100 value calculated from extracted perceptions in AI responses: $$\text{Sentiment Score} = \frac{\text{Positive perceptions}}{\text{Positive} + \text{Negative perceptions}} \times 100$$ Score ranges:

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Sentiment by LLM

Different AI models often have different tones toward the same brand. The per-LLM breakdown shows:

• Positive %: share of positive perceptions for that model
• Positive count / Negative count: raw mention volumes

Use this to identify model-specific narratives. A brand can be recommended by Claude but flagged as expensive by ChatGPT. The fix differs per model because the sources each model relies on differ.

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Perception categories

Pierview classifies extracted perceptions into 8 categories: Each category shows a tone (positive, negative, neutral, or mixed) based on the distribution of mentions within it. Categories with more data are weighted more heavily.

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How to use category data

A negative score in Pricing & Value means AI responses frequently surface cost objections. The fix is content that directly addresses value: comparisons, ROI calculators, pricing FAQs. A negative score in Products & Features often points to a specific missing feature being surfaced repeatedly. Check the extracted facts to see which feature.

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Brand strengths and weaknesses

Pierview extracts the specific phrases and attributes being mentioned, then surfaces the top:

• Strengths: most frequently cited positive attributes (e.g., “fast onboarding”, “best for enterprise”)
• Weaknesses: most frequently cited negative attributes (e.g., “limited integrations”, “steep learning curve”)

Each item shows:

• How often it appears across responses
• Which LLM(s) surface it
• The prompt context it appeared in

This is the most actionable output from the Perception dashboard. If a weakness appears across many prompts and LLMs, it’s a signal worth addressing in content.

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Competitor sentiment comparison

The competitor panel shows sentiment scores and perception counts for each tracked competitor, side by side with your brand. For each competitor you see:

• Sentiment score (0–100)
• Positive fact count
• Negative fact count

This reveals whether you’re losing on perception even when you appear alongside competitors. If a competitor consistently scores 20 points higher, inspect their extracted strengths. That’s the narrative gap to close.

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Filtering

The Perception dashboard supports:

• Time range: filter to last 7, 14, or 30 days to spot trend changes after a content update or launch
• LLM filter: isolate a single model to understand model-specific behavior

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