Not just positive or negative. Is AI building the right narrative?
AI models rarely speak badly about brands, that makes standard sentiment scores meaningless. Prominence benchmarks your tone against the category average to tell you when you're truly overperforming, holding neutral ground, or falling behind your peers.
Kia
Tesla
BYD
Polestar
LLMs don't speak badly about brands. That makes standard sentiment almost useless.
Traditional sentiment tools ask a binary question: is the tone around your brand positive, neutral, or negative? In web search, that distinction is meaningful. In AI-generated answers, almost everything sits in positive or neutral territory. The model is designed to be helpful, not to pass judgement.
A real problem only shows up in comparison. If the entire category averages 62 and your brand scores 46, that gap is the signal. An absolute score of 46 looks fine on paper; relative to your category, it is a red flag.
Prominence builds a dynamic category baseline from the same responses we collect for your competitive set. Every response is scored semantically and compared to that baseline, placing it in one of three tiers: Overperforming, Neutral, or Underperforming.
The result is a sentiment signal that actually moves, trends you can track week over week, and a benchmark that tells you whether your brand is capturing the narrative or ceding it.
From AI response to sentiment signal
Connect your brand and category
Set up your brand, the competitors you want to benchmark against, and the prompt set that covers your category. This configuration drives both your visibility metrics and your sentiment analysis without any additional setup.
AI responses collected at scale
Prominence continuously collects AI-generated answers across ChatGPT, Google AI Overviews, Google AI Mode, Gemini, Perplexity, and Copilot. Every response mentioning your brand or category is captured for semantic analysis.
Semantic sentiment scoring
Each response is scored semantically, not by keyword matching, but by evaluating the meaning and tone of the language the model uses. Scores reflect the warmth or caution of the description in context, not just the presence of positive or negative words.
Category baseline and tier assignment
Your brand score is compared against the dynamic category average drawn from your competitive set. Responses that sit significantly above average are classified as Overperforming. Those within the typical range are Neutral. Those below are Underperforming.
Trends, model breakdown, and segment drill-down
Weekly trend charts show how your sentiment tier mix evolves over time. Segment views let you isolate performance by product dimension or use case. Model comparisons reveal where specific AI platforms treat your brand differently from the rest.
The full picture of AI-generated perception
Sentiment Score
A 0-to-100 index calibrated against your category baseline. Tracks period-over-period change so you can see whether the narrative is drifting in your favour or away from it.
Sentiment Trends
Area chart showing how the Overperforming, Neutral, and Underperforming share of responses evolves week over week. Spot inflection points before they become entrenched patterns.
Sentiment by Model
Breaks down your sentiment score and tier distribution per AI platform. Understand whether a narrative gap is universal or tied to how one model specifically describes your brand.
Competitor Benchmark
See the full sentiment distribution for every brand in your competitive set. Identify who is consistently overperforming and what narrative patterns might be driving their advantage.
Segment Performance
Drill into sentiment by the specific dimensions you track, olfactory notes, longevity, recommendations, or any other category segment. Find where perception is strong and where it is losing ground.
Narrative Activation
Sentiment shows where the gap is. Combine it with Grounding Source Prominence to identify which domains and media are shaping the narrative, and act.
“A sentiment score in isolation is a vanity metric.
The signal is whether you beat the category, not whether AI was polite.”
AI models are recommendation engines, and recommendation engines are built to be agreeable. They rarely speak badly about a brand, so almost everyone scores "positive". That is exactly why a raw sentiment number tells you nothing about where you actually stand.
The brand teams we work with had all hit the same wall: their dashboards were green, yet they could feel the narrative slipping. The flaw was never the data, it was the absence of a reference point. Positive against what? Liked more than whom?
Sentiment here is measured against the category, not the brand alone. We compute a live average across your competitive set and measure you against it, so every response lands as overperforming, neutral, or underperforming. Suddenly the metric moves, and when it moves it means something.
From there it gets actionable: track the tier mix week over week, break it down by model, and drill into the segments that define your market. That is how a soft qualitative signal becomes a number a brand team can actually steer.
Andreas Niessen
Co-Founder, Prominence AI
Start measuring what AI actually says about your brand
Connect your brand in minutes. AI Sentiment Analysis runs on responses you're already collecting, so there's nothing extra to set up.
Frequently Asked Questions
Everything you need to know about AI Sentiment Analysis and brand perception measurement.