We automated candidate assessments to save time and allow recruiters to prioritize critical tasks like building relationships with clients and candidates.
Optimizing the candidate assessment process reduced time to first candidate introduction and time to hire by 20%.
As Bolster expanded its executive search business, we looked for ways to enhance the recruiting team's effectiveness. During user interviews with recruiters, we discovered that writing candidate assessments was one of the most time-consuming aspects of their roles.
For this project, it was essential for recruiters to trust the AI output from the scorecards. To address this, we created prototypes so recruiters could review the output, provide feedback, and enable us to iterate on it before developing any UI on the platform.
Creating prototypes allowed us to test an early concept that recruiters would want to rate candidates based on their CXO Persona type. In the testing phase, recruiters and clients didn't find the assessments valuable. Ruling this out in the prototype phase saved us valuable time later in the development phase.
Data didn't give us a complete picture of a candidate's background. We learned that screening calls gave recruiters additional insights that would influence scores. We updated the prototype to incorporate candidate notes already stored on the platform, which provided us with more accurate candidate scoring.
We automated the candidate assessment process by creating an interface that allows recruiters to input freeform candidate requirements. Using these requirements, we leveraged OpenAI to analyze candidates' experiences and data about the companies they have worked for. This solution enables recruiters to quickly evaluate and score candidates while also eliminating any unintended bias.