Case Study: a global education brand increases content discoverability with Primer’s NLP platform

Generating Topics For A Large Content Library To Increase Discoverability Using NLP

A globally-known education brand faced the challenge of manually classifying and tagging its vast library of over 100,000 articles, a slow and inconsistent process that created a bottleneck for its expert staff. To make their content more discoverable for professors and researchers, they partnered with Primer to leverage its NLP Platform to automate this task.

Primer implemented a Topic Text2Text model that was customized to the customer's specific ontology of 700 topics. The solution used an advanced workflow to process long-form documents, generating appropriate tags that were then delivered via an API. This automation significantly streamlined the manual effort, with 85% of the model's tags being accepted by human reviewers, increasing staff efficiency and moving the organization closer to its goal of full automation.