Hiring and Recruiting
Recruiters and hiring leads10 min read
AI Recruiter Assistant and Candidate Pipeline
The hiring assistant helps recruiters shortlist, compare, filter, rediscover, and document candidate decisions with structured, reviewable output.
Natural-language recruiter actions
- Shortlist the top backend engineers for a role.
- Filter candidates by skill, years of experience, seniority, or location.
- Extract phone numbers, email addresses, and key profile fields.
- Compare selected candidates and surface missing qualifications.
- Rediscover strong applicants from earlier roles.
Candidate lifecycle states
- New, parsed, screened, shortlisted, interview, on-hold, rejected, hired, withdrawn, and discarded.
- Discarded and withdrawn candidates stay auditable but are excluded from default ranking context unless explicitly requested.
How to read fit scoring well
- Look at evidence and reason codes, not just the score.
- Use scoring to prioritize review order rather than making final decisions automatically.
- Check whether a weak score comes from missing requirements, poor source quality, or incomplete candidate documents.
High-volume recruiting practices
- Use shared note templates and assistant playbooks for consistent screening quality.
- Define default exclusions carefully so discarded or withdrawn candidates do not pollute new ranking requests.
- Use rediscovery when opening similar roles instead of rebuilding the slate from zero.
Common recruiting workflow questions
Why are discarded candidates excluded from default ranking?
The default ranking behavior is designed to keep inactive candidates from skewing recruiter recommendations while preserving their audit trail for explicit review later.
Can the assistant replace recruiter review?
No. The assistant is designed for speed, structure, and consistency. Final hiring decisions should still use recruiter judgment and role-context understanding.