AI Visibility
AI Sentiment
Being mentioned is only half the story. What the assistant says about you next is the half that closes or kills the deal.
A mention with a caveat is not a win
Models rarely name a brand without characterising it. Expensive, best for enterprise, limited integrations, hard to set up: a single stock phrase, repeated across millions of conversations, quietly filters who ever contacts you.
Some of those characterisations are fair. Many are outdated, drawn from an old review or a competitor comparison written years ago. We find out which is which, then supply the current, verifiable facts that give models something better to say.
What sentiment work involves
- 01
Sentiment baseline
How each major engine describes your brand today, including the adjectives and caveats that keep recurring.
- 02
Source tracing
Where an unfavourable characterisation comes from: an outdated review, a stale comparison, a competitor page or your own copy.
- 03
Correction plan
The facts to publish, the pages to update and the sources to approach in order to move the description.
- 04
Proof and specifics
Concrete, checkable claims about pricing, capability and fit, so models have accurate material to draw on.
- 05
Ongoing monitoring
Repeat measurement so you can see the language shift and catch new inaccuracies early.
Sentiment questions
Can inaccurate descriptions actually be fixed?
Often, yes, when they come from retrievable sources. Update the source material and the current facts start appearing. Characterisations baked into training data change more slowly and need a broader body of corrected evidence.
What if the criticism is fair?
Then it is product feedback, and we say so. We can help you frame the trade-off honestly, but we will not help you paper over something buyers will discover anyway.
How do you measure sentiment objectively?
A fixed prompt set, run repeatedly across engines, with responses scored on the same criteria each time so the trend is comparable month to month.