Introducing Relevance: An AI Model Tuned To Your Brand, Keeping Your Data Clean, 24/7

The quality of your insights is only as good as the quality of your data. And in social intelligence, volume and noise often go hand in hand. Which means that no matter how strong your boolean game is, we know you’re spending hours and hours every week cleaning datasets before doing your insight magic.

That's why today we're introducing Relevance, a powerful new Vertical AI model for Pulsar TRAC designed to help you keep your data cleaner and focused, while you sleep.

Relevance uses semantic affinity to bypass boolean queries and filters to automatically assess whether a piece of content is a match for your specific brief, filtering out the noise so you can get to the insights faster.

Your Brand. Your Brief. Your Model.

Relevance isn't a generic filter. It's a model configured and customized around your organization's specific definition of what belongs in your data and what doesn't, accounting for your brand's context and the nuances of the topic areas you are analyzing. 

That matters more than it might seem. Search for a retail brand, a policy area, or a consumer product, and you're immediately fighting homonyms, tangential mentions, and irrelevant chatter. Once Relevance is set up on your domain, it automatically tags content in your searches as relevant or irrelevant, keeping your dataset clean and analysis-ready from the moment data comes in. And because the model is deployed across your domain, that brand-specific intelligence scales consistently to every search your team runs.

Built for the Scale Enterprise Teams Face

For teams tracking brand health across multiple markets, monitoring campaigns across dozens of searches, or policy narratives at the top of the news agenda, manual relevance filtering doesn't scale. Relevance does.

The result is cleaner data at the source. Cleaner data means sharper insights, faster turnaround, and greater confidence when presenting findings to clients or leadership. For large agency groups, that means consistent relevance standards across accounts. For government and public sector analysts working on high-stakes topics, it means data you can trust.

Crucially, content not deemed relevant is not deleted, it’s just not tagged as relevant. That distinction matters. Keeping rejected content available for review means teams can audit the model's decisions to fine-tune it as new events and campaigns come through, spot patterns in the general structure of the conversation, and run any future analysis on the not relevant datasets. 

The Data Foundation for Everything Else

Relevance has been designed to work across the Pulsar platform. It’s not just a TRAC feature, but a foundation that improves the quality of everything built on top of it.

Cleaner data on Pulsar TRAC means more accurate audience segmentation and narrative clustering analysis, more focused and accurate reporting, and more trustworthy inputs for the agentic workflows you’re running on your data, like Crisis Oracle and CLEAR. 

Whether your team is tracking public opinion around key topics, monitoring your brand’s reputation, or sourcing innovation and marketing insights, the intelligence is only as good as what feeds it. Relevance makes sure what feeds it is right.

Interested in learning how relevance filtering can help you better understand your audiences? Fill in the form below to see it in action.