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.
How Does the Model Work?
Once your custom model has been deployed to a search, the Relevance model automatically analyzes every piece of content and applies a label to each post, indicating whether it is relevant or irrelevant to your topic.
Users can then use the Relevance filters to review results and remove irrelevant posts if needed. This gives users full visibility and control over their dataset, particularly in cases where the model may occasionally assign an incorrect label. By allowing users to review and refine results, this approach not only improves data quality within a search but also helps us continuously fine-tune the model to achieve higher levels of accuracy over time.
Key Benefits of the New Relevance Model
Cleaner Datasets: By automatically identifying and separating irrelevant content, the model ensures that your analysis is based only on relevant data. This is especially powerful when searching for broad or ambiguous topics, such as "O2", Three, or "Apple", where the potential for noise is high.
Increased Efficiency: The model automates the time-consuming process of manually sifting through large volumes of data to determine relevance. Content is automatically tagged with a "relevant" or "irrelevant" label, and you have the option to automatically move irrelevant content to the Rejected folder for further review.
Sharper Insights: With cleaner data, you can have greater confidence in the accuracy and relevance of your insights. This allows you to make more informed decisions and develop more effective strategies based on a true reflection of your audience's conversation.
Fully Customisable: We understand that relevance is unique to every brand and every brief. That's why our team will collaborate with you to fine tune the model and set of instructions tailored to your specific brief. Once configured, the model can be deployed to any search within your domain.
Who Will Find This Useful?
Relevance is a valuable tool for any Pulsar client looking to improve the quality and efficiency of their data analysis. It is particularly beneficial for:
Enterprise Brands and Agencies: Teams that are constantly monitoring brand health, tracking campaigns, and analyzing consumer conversations will find this model invaluable for cutting through the noise and focusing on what's truly being said about their brand.
Researchers and Analysts: Anyone conducting in-depth research on broad topics will save a significant amount of time and effort by using the model to pre-filter their datasets.
Teams Working with High-Volume Searches: For searches that generate a large volume of data, the Relevance Model is essential for managing the data and ensuring that the analysis is not skewed by irrelevant content.
Getting Started
To get started with the new Relevance Model, simply reach out to your account manager or our support team. We'll work with you to understand your needs and create a custom Relevance model that's tailored to your specific requirements.
We're confident that the new Relevance Model will be a game-changer for how you work with data in Pulsar.