QLFY builds AI‑powered autopilot agents that automate the repetitive “grind” of hiring—research, outreach, screening, and follow‑through—so decision‑makers can focus on judgment.
Funding
Funding not disclosed
Founders
Product
Problem
Hiring teams spend a large portion of their time on repetitive tasks such as candidate research, outreach, screening, and follow‑up, which adds little strategic value and slows the hiring process.
Solution
QLFY offers AI‑powered autopilot agents that take over the “grind” of hiring. Users submit a role brief in any format, and the proprietary learning engine interprets it, gathers market and company context, and selects optimal sourcing strategies. The system continuously calibrates based on early feedback, producing a client‑ready shortlist with transparent reasoning for each candidate. By automating research, outreach, and screening, QLFY enables decision‑makers to focus on judgment and final selection.
Target Audience
QLFY is aimed at talent acquisition leaders, senior recruiters, and hiring managers in high‑growth technology and product companies who need to scale hiring while maintaining decision quality.
Features
- Natural‑language brief ingestion that understands text, voice, or document inputs without requiring structured forms
- Automated market and company research to enrich the role context, including compensation bands and hidden constraints
- Adaptive sourcing strategy that selects channels and target companies based on historical success for similar roles
- Continuous learning loop that refines candidate ranking after user feedback on early profiles
- Generated shortlists with detailed candidate profiles, ranking rationale, and outreach instructions, all traceable in an audit trail
- Proprietary self‑improving AI engine that retains and leverages the full corpus of past searches for better future performance