AI-Powered Scorecard
Automated candidate assessments, allowing recruiters to prioritize critical tasks like building relationships with clients and talent.
Bolster
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The Challenge

As Bolster expanded its executive search business, we sought to improve our tools to help the recruiting team operate more effectively. Through our discovery meetings with recruiters, we learned that assessing candidates was one of the most time-consuming aspects of their jobs. 

The Process

For this project, it was essential for recruiters to trust the AI output from the scorecards. To address this, we created engineering prototypes so recruiters could review the output, provide feedback, and enable us to iterate on it before developing any UI on the platform.

As Bolster expanded its executive search business, we sought to improve our tools to help the recruiting team operate more effectively. Through our discovery meetings with recruiters, we learned that assessing candidates was one of the most time-consuming aspects of their jobs.

Airtable and Streamlit prototypes were used to validate the scorecard concept with recruiters.
Notes kept throughout the discovery and research process.
Early wireframe concepts for scorecard scale.

Solution

We automated the candidate assessment process by creating an interface that allows recruiters to input 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. 

Requirements are added at the project level and processed for individual candidates as needed.
Candidate Scorecard Tab: processed, edit, and empty state.

Results

Optimizing the candidate assessment process for our recruiters allowed them to focus on other vital tasks and reduced time to first candidate introduction and time to hire by 20%.