Nexora | Track AI-Powered Rule-Based Interview Automation

Revolutionizing recruitment through hybrid AI and rule-based automation for fair, transparent, and efficient candidate evaluation.

Comment

Inspiration

Recruitment has become increasingly complex, with HR teams spending countless hours on initial screenings while candidates struggle to showcase their true potential. Many hiring systems lack fairness, consistency, and scalability. Nexora Track was built to bridge this gap, automating the initial interview process while maintaining transparency and explain ability through a rule-based + AI hybrid approach.

What it does

Nexora Track automates initial job interviews through a three-phase structure (technical, non-technical, and general). Recruiters can input or scrape job data, generate smart interview questions, and automatically evaluate candidate responses using a rule-based scoring engine enhanced by AI models.

Core Features: 🧩 Multi-phase interview automation (technical, non-technical, general) 🤖 AI-assisted question generation using Gemini API 🧠 Hybrid evaluation engine (Rule-based + Semantic AI Scoring) 📊 Candidate ranking and analytics dashboard (Chart.js) 💬 Self-assessment mode for fresh graduates 🔐 Secure authentication with Google Login (Firebase / Next AUTH) 🧾 Exportable reports and feedback summaries

How I built it

  • Designed a modular rule-based engine to evaluate candidate answers for relevance, completeness, and keyword match.
  • Integrated Gemini API to assist in question generation and scoring explanation.
  • Built a REST API backend in Node.js and connected it with a React frontend.
  • Created dashboards for both recruiters and candidates with interactive charts and analytics.

Challenges I ran into

  • Fine-tuning the rule-based scoring alongside AI feedback without bias.
  • Structuring interview question templates dynamically based on job descriptions.
  • Managing multi-user authentication (Recruiter, Candidate, Admin) securely.

Accomplishments that I'm proud of

  • Successfully automated the entire initial interview process.
  • Built a transparent system that blends human-like evaluation with explainable AI logic.
  • Created a scalable, cross-platform architecture ready for enterprise adoption.

What I learned

During this project, I gained deep experience in designing and implementing rule-based automation systems integrated with modern AI APIs. I learned how to combine logical evaluation mechanisms with AI-driven assistance to achieve explainable and fair candidate assessments.

  • This project also helped me improve my skills in:
  • Full-stack web development using React, Node.js, and MongoD
  • API integration and testing (especially with Gemini API and Firebase)
  • Building scalable backend architectures for multi-role systems
  • Applying data visualization tools like Chart.js for real-time analytics
  • Designing user experiences that balance automation with transparency
  • Most importantly, I learned how to design a system that blends human reasoning with AI intelligence, ensuring both efficiency and fairness in recruitment.

What's next for Nexora Track AI-Powered Rule-Based Interview Automation

  • Add multimodal AI (voice and video-based interviews).
  • Integrate resume parsing with AI keyword matching.
  • Introduce adaptive learning to improve the scoring engine from recruiter feedback.
  • Expand integrations with LinkedIn, Google Jobs, and ATS platforms.
Share this project:

Updates