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AI Interview Facilitator automates first-round interviews end-to-end. A candidate uploads their resume; Gemini generates a schema-validated question set tailored to that exact resume and target role; an interviewer enables the session; and the candidate takes the interview on-site or remotely. During remote interviews, questions are read aloud via text-to-speech, spoken answers are transcribed by a Whisper speech-to-text service, and camera frames are periodically analysed by a MediaPipe computer-vision service for confidence, confusion, gaze direction and multiple-face incidents. Everything is fused into a structured verbal + non-verbal evaluation report generated by Gemini and stored in Firestore. Built as an Expo/React Native app orchestrating three Python microservices and Firebase.
First-round interviews don't scale. Recruiters spend hours running near-identical screening calls, every interviewer scores candidates differently, remote interviews are hard to proctor, and candidates get little structured feedback. Generic question banks make it worse — questions rarely reflect what's actually on a candidate's resume.
AI Interview Facilitator turns the whole first round into an automated, consistent pipeline: questions are generated from the candidate's actual resume and target role, the interview runs itself on-site or remotely with spoken questions and recorded answers, behaviour is analysed on camera for integrity, and both sides get an objective, structured AI evaluation report instead of a gut feeling.
Screening becomes scalable and fair: every candidate for a role answers questions tailored to their own experience under identical conditions, integrity is protected by camera-based behavioural analysis rather than a human proctor, and hiring decisions start from a consistent, evidence-backed report instead of interviewer memory.
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