Why framing matters, not just mention rate
A brand can appear in 40% of category AI queries and still lose to a competitor that appears in 30%. Mention rate alone is misleading. If the competitor is consistently framed as the top recommendation and the first brand is hedged with caveats, the lower-frequency brand wins.
Strong positive framing
"[Brand] is widely regarded as the best option for [use case]. It offers [specific benefits] and has strong reviews on G2 and Capterra."
Impact: Drives purchase intent. Creates a clear recommendation that the user acts on.
Hedged framing
"[Brand] is a popular option, though some users note a learning curve. It may be worth considering depending on your specific needs."
Impact: Visibility without conviction. User sees the brand but without the recommendation weight.
Negative framing
"While [Brand] has a large user base, it has received criticism for pricing and customer support issues. Alternatives may offer better value."
Impact: Active harm. Being mentioned with negative framing can reduce purchase intent below baseline.
Incorrect framing
"[Brand] specializes in [wrong use case/incorrect pricing/outdated information]."
Impact: Misleads potential customers. Damages consideration among buyers who accept the AI description as accurate.
What ArtificialPulse's sentiment tracking covers
Mention framing classification
Each brand mention is classified as positive recommendation, neutral mention, hedged mention, or negative mention.
Position in response
First mention vs. second vs. buried. Position within the AI response correlates with user attention and action rate.
Accuracy tracking
Are the descriptions of your brand accurate? Incorrect service descriptions, wrong pricing, outdated information - all flagged.
Framing trend over time
Is your AI framing improving? Weekly sentiment data plotted over time shows whether optimization work is improving how you're described, not just how often.
Competitor framing comparison
How is your brand framed relative to competitors in the same response? Relative positioning within a single AI answer matters.
Category-specific query framing
Which query types produce the strongest framing? Which produce hedged or negative framing? Query-level breakdown drives targeted optimization.
What drives framing quality
AI framing reflects the third-party corpus of sentiment signals about your brand. Honestly, most brands are surprised by what they find. The most common sources of poor AI framing:
Negative or mixed reviews on G2/Yelp/Google
Fix: Review response strategy + improvement of the underlying issues that drive negative reviews. Review platform signals directly feed AI framing.
Outdated information in high-ranking articles
Fix: Update or outreach to publishers with outdated descriptions. Perplexity retrieves these articles in real time - an article saying you're "$X/month" when you're now "$Y/month" creates inaccurate AI framing.
No Wikipedia/Wikidata entity or inaccurate entity data
Fix: Create or correct your Wikidata entry. AI models use entity data for brand descriptions - inaccurate entity data produces inaccurate AI framing.
Strong competitor presence in comparison articles
Fix: If "Brand vs. Competitor" articles consistently favor the competitor, AI framing in comparative contexts will reflect this. Address the root signal.
See how your brand is framed in AI responses
Free audit shows your AI Visibility Score and includes framing analysis - how your brand appears when it's mentioned, not just that it's mentioned.