Recruiters and colleges struggle to screen large numbers of candidates fairly and efficiently. Traditional hiring methods are slow, biased, and do not properly evaluate real skills. ScreenerPro was built to solve this problem using AI by automating candidate screening, mock interviews, proctoring, and performance analysis in one platform.
What it does
ScreenerPro is an AI-powered hiring and interview platform that helps recruiters and institutes assess candidates through:
AI mock interviews with camera and microphone enabled
Real-time proctoring (tab switching, full-screen enforcement, cheating detection)
Resume-based dynamic questioning
Skill, confidence, and behavior scoring
Public job board and hiring campaigns
Mentor and SIG modules for colleges
Verification links for interview reports
The platform provides detailed insights into candidate performance and significantly reduces manual screening effort.
How we built it
We built ScreenerPro using Flutter Web for a responsive UI on both desktop and mobile. The backend uses Firebase Authentication and Cloud Firestore to securely manage users, interviews, reports, and job data. We integrated AI APIs such as Gemini and OpenAI for question generation, answer evaluation, confidence scoring, and smart interview flows. Proctoring is implemented using browser APIs and Flutter Web listeners to detect tab switching, focus loss, and full-screen exit.
Challenges we faced
Implementing secure proctoring on Flutter Web
Designing real-time AI interview flows that feel natural
Structuring Firestore data for scalable hiring campaigns
Handling Google Sign-In and Firebase authentication issues
Managing performance while using video, audio, and AI processing together
What we learned
Designing secure online assessment systems
Real-world integration of Firebase and REST APIs
Using AI in real production features
Working around Flutter Web limitations
Building a scalable SaaS architecture
Built With
- computer
- css
- dart
- firebase
- flutter
- gemini
- javascript
- restapi
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