Brief each interviewer, and find where candidates drop out
In the same xAI guide, one bot posts a single Slack note before each interview with who the candidate is, why the team is interested, the stage and what to focus on. Another looks across the interview loop for where candidates fail and why, and turns that into questions recruiters can ask at the first screen.
What they did
In an xAI guide, the company's recruiting team describes bots built at a team offsite. Preppy posts one Slack note before each interview that pulls candidate context from Notion, LinkedIn and the applicant tracking system. eVal looks across the interview loop for where and why candidates fail.
What happened
The team says interviewers get who the candidate is, why the team is interested, the stage, focus areas and what has already been covered in one place. eVal's findings become questions recruiters can ask at the first screen, so problems are caught before interviewer time is spent.
Keep in mind
This is the vendor's own team writing on its own site, with no numbers on time saved or hiring outcomes. Using candidate data across tools also raises privacy questions the guide does not cover.
Try it with your agent
One hour before each interview on my calendar, pull the candidate's details from <your applicant tracking system> and <other notes location> and post a short brief to <chat channel>: who they are, why we are interested, interview stage, what earlier rounds covered, and what this interviewer should focus on. Once a month, look across interview feedback for <role> and tell me at which stage candidates most often drop out and why, with two or three questions we could ask earlier.
Replace the <angle brackets> with your own details.