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Amplifyr tracks how a brand shows up across AI models, and a Neotask agent connected to it turns that tracking into something a marketing or comms team can just ask about in plain English. Want to know your share of voice against competitors, and it pulls that number; curious how you rank in your industry, and it checks; wondering what questions people are actually asking that surface your brand, and it lists them; need a read on sentiment or which citations are driving visibility, and it reports on that too. Everything stays scoped to the requesting organization's own data, so this isn't a generic AI visibility lookup, it's a running answer service for a team trying to understand its footprint across AI models specifically, as opposed to traditional search rankings that most tools were built to track. A comms lead can ask the same kind of question they'd ask a search analytics tool, just aimed at a completely different surface. As AI answers become a bigger part of how people first hear about a brand, having a direct line to that specific slice of visibility, separate from search console numbers, gives a team a clearer sense of where its reputation is actually forming. It also means a question that would otherwise require pulling several reports together, like sentiment alongside ranking alongside citations, gets answered as one connected picture instead of three disconnected charts.
| get_share_of_voice | Reports how often the brand appears relative to competitors across AI models, scoped to the organization's tracked data. |
| get_industry_ranking | Returns the brand's current ranking within its industry category based on AI model visibility. |
| list_related_questions | Surfaces the questions people ask that lead to the brand being mentioned in AI answers. |
| get_sentiment | Reports overall sentiment associated with the brand's mentions across tracked AI responses. |
| get_citations | Lists the sources being cited when the brand comes up in AI answers, useful for spotting influential references. |
A marketing lead needs numbers ahead of a monthly review and asks the agent for share of voice and industry ranking trends. The agent pulls both, scoped to the team's organization, and adds in a quick read on sentiment for the period. Sentiment has ticked up slightly, which the lead wants to highlight as a positive sign. Wanting one more detail for the deck, they ask for the top citation sources behind the brand's recent mentions, and the agent lists a handful worth calling out. The lead builds their review deck around those four data points without pulling three or four separate reports from different places.
A team notices a competitor seems to be showing up more in AI answers lately and asks the agent to investigate. The agent checks share of voice trends and pulls the related questions currently driving visibility in that category. The pattern points to a specific set of questions the competitor answers better than the team currently does. The team also asks the agent to check sentiment around those same questions, and it turns out responses mentioning their own brand skew slightly more positive even where they show up less. That gives the team something concrete to address in their next round of content rather than a vague sense of falling behind.
No, it's focused specifically on how a brand shows up across AI model responses.
Yes, the agent can list the specific questions people ask that surface the brand.
It's scoped to your organization's own tracked data rather than a generic industry-wide report, so the numbers reflect what's actually been tracked for your brand specifically.
Yes, sentiment is one of the things the agent can report on alongside share of voice and rankings.
They're the sources AI models reference when your brand comes up, which the agent can list out to show what's actually driving a given mention.
Share of voice comparisons are framed against competitors, so competitor context is part of most of these queries by default, even when the question is really about your own brand's standing.