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Automate candidate screening. Create a quiz and set evaluation prompts for each question. Equip AI will evaluate candidates' text, audio or video responses and rank them. Effective for recruiters. Convenient for candidates.
Equip logs every interview, score, and manual override, creating a complete audit trail for compliance. Detailed explanations for AI scores are documented, and recruiters can justify decisions to hiring managers and compliance teams. SOC 2 and GDPR compliance built-in, supporting organizations with regulatory hiring requirements.
Equip provides pre-built role-aware interview templates (e.g., AI-native Developer, GTM Engineer, Chief of Staff) with predefined competency evaluation criteria. Recruiters can use templates as-is or customize evaluation prompts for their specific needs. This eliminates the need to design interview questions and scoring rubrics, enabling rapid deployment of role-specific assessments.
Equip AI Interviews enable recruiters to replace manual phone screens with asynchronous one-way or real-time conversational AI interviews. Candidates answer via text, audio, or video; AI instantly scores responses across 90+ languages. This scales screening from dozens to 10,000+ candidates simultaneously at $1-3 per candidate, eliminating recruiter time spent on repetitive screening calls.
Equip generates interview results instantly upon candidate submission, with structured competency evaluations and transparent reasoning. Candidates receive immediate feedback, improving experience. Results flow automatically into Equip's native ATS, eliminating manual data entry and enabling recruiters to move qualified candidates to next stages within minutes rather than days.
Equip's AI scoring applies identical evaluation criteria to every candidate's text, audio, or video responses, removing human bias from initial assessments. Recruiters define evaluation prompts (e.g., 'candidate must demonstrate leadership in crisis situations'), and AI scores consistently. Detailed explanations for each score allow manual override and continuous model improvement, ensuring fair and transparent evaluation.