An AI sentiment audit checks how AI engines frame your brand and traces negative framing to its source. Here is how to run one across engines.
An AI sentiment audit is a structured review of how AI engines frame your brand when they mention it — favorably, neutrally, or negatively — across your buyer-intent prompts and every engine you track. Rather than counting mentions, it reads the tone of each one and, crucially, traces any negative framing back to the sources driving it, so the output is a diagnosis rather than just a number.
The reason to run one is that being mentioned is not the same as being recommended. A brand cited with a caveat has a different, and often more urgent, problem than one that is simply absent, and only a sentiment audit surfaces it.
It is easy to treat any mention as a win. But two brands with identical mention rates can have opposite outcomes if one is consistently recommended and the other consistently qualified. Tone is what separates those cases, and it hides inside answers you would otherwise count as successes.
That is why sentiment deserves its own audit. A rising mention rate paired with sliding sentiment can look like progress on a dashboard while quietly costing deals, and only a deliberate look at framing catches it.
A sentiment audit reads each answer that names your brand and sorts it into one of three buckets.
Aggregating those classifications across prompts and engines produces your sentiment profile — the share of mentions working for you versus against you — and breaking it down by engine and prompt shows exactly where the negatives cluster.
The most useful part of an audit is not the score but the cause. AI engines summarize the sources they can find, so when they describe you negatively, they are usually reflecting something specific: a stale spec, a cluster of unaddressed complaints, or a competitor's comparison page that only tells their side.
Identifying that source is the whole point, because it converts a vague worry into a concrete fix. You cannot argue an engine out of its framing, but you can change the inputs it reads — and the audit tells you which inputs to change.
A sentiment audit is most useful when you read the framing in context rather than as a raw label. Not every neutral mention is a problem, and not every caveat is unfair. An engine noting that your product is built for larger teams is only negative if your buyers are smaller teams; for enterprise buyers it may be a selling point. The audit gives you the classification, but judgment turns it into a priority.
The cases that deserve the most attention are the ones where the framing is both unfavorable and inaccurate, or where it steers a buyer toward a competitor for the wrong reason. Those are the negatives that cost deals, and they usually trace to a specific source you can address.
It also helps to look at sentiment relative to competitors. If engines frame everyone in your category with the same caveat, that is a category perception to work on collectively; if they single you out, that is a brand-specific source to fix. The audit surfaces which situation you are in.
An audit is a point-in-time diagnosis. Once you have acted on it, ongoing sentiment tracking confirms the framing improved and catches new negative sources before they spread. The two fit together: the audit tells you where you stand and why, and tracking keeps you there.
TrueCite classifies the sentiment of every brand mention across the nine engines it supports and shows which prompts produce negative framing, so you can trace each one to its source and confirm the fix over time. An AI sentiment audit is how you find out not just whether AI mentions your brand, but whether it is helping or hurting you.
Run your AI sentiment audit with TrueCite — 7-day free trial, no card required.
Using TrueCite? See the Brand Sentiment Analysis docs →