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Project Name:

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Team Name: Xentry

Team Leader Name: Arpit Sengar

Team Members: Tanisha Bisht

Problem Statement:

Traditional job portals primarily function as static opportunity aggregators. While they provide access to listings, they lack context-aware personalization, adaptive recommendation engines and end-to-end application orchestration. Consequently, undergraduates face high cognitive load, process redundancy and application fatigue, often resulting in missed opportunities.

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A further limitation lies in the absence of equitable career enablement mechanisms. Access to mentorship pipelines, network capital and data-driven guidance remains uneven, reinforcing disparities in employability and professional growth.

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To mitigate these challenges, there is a critical need for an autonomous, agent-driven architecture capable of workflow automation, intelligent opportunity matching and adaptive application material generation. Leveraging agentic AI paradigms including autonomous information retrieval, contextual content generation and feedback-driven optimization such a platform can provide a scalable solution.

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This system would deliver an efficient, adaptive and equitable application ecosystem, thereby reducing friction, enhancing decision support, and bridging systemic gaps between undergraduates and employment opportunities.

Solution:

1. A streamlined interface that minimizes the job application process to quick interactions, making it efficient and user-friendly for undergraduates.
2. Automated form filling, resume tailoring, and application submissions by leveraging data from LinkedIn, GitHub, Resume and past projects.
3. Uses AI-driven algorithms to recommend opportunities aligned with a student’s skills, experiences and aspirations, ensuring higher relevance and success rates.
4. Employs NLP-based models to dynamically generate and personalize application materials for each opportunity.
5. Analyzes application outcomes and recruiter feedback to iteratively refine future recommendations and submissions.
6. Ensures fair and adaptive job application support, bridging gaps for students who lack mentorship or professional networks.

System Architecture:

TECHNOLOGY STACK

• Frontend: React JS, React Native, Expo, Tailwind CSS, Framer-Motion
• Backend: REST: FastAPI, DB: Supabase
• AI and ML: Llama, LangChain, TensorFlow, HF Transformers (NLP fine-tuning)
• API Services: LinkedIn API, GitHub API, Resume Parsing API, Job Board APIs
• Cloud and Deployment: AWS, Docker, Kubernetes, GitHub Actions
• Add-Ons: Redis (caching), Postman (API testing), Clerk

METHODOLOGY & IMPLEMENTATION

• User Profile Integration: Students register via the Expo app, with data imported from LinkedIn, GitHub, resumes and projects using APIs and parsing modules.
• Opportunity Discovery: The agentic AI backend (powered by Parlant for reliable agent orchestration) fetches and ranks jobs via LinkedIn and job board APIs.
• Swipe-to-Apply Interaction: A minimal mobile interface enables quick interest expression, reducing application friction.
• Automated Submission & Resume Generation: AI fills forms, tailors resumes and creates dynamic cover letters using LLaMA, LangChain and HF Transformers.
• Feedback Loop: Recruiter feedback refines recommendations, improving accuracy over time.
• Deployment & Scalability: Hosted on AWS with Docker & Kubernetes; Supabase and MongoDB manage data; Redis & ElasticSearch enable caching and search; Clerk ensures authentication.

SYSTEM FLOW

FEASIBILITY & MARKET APPLICATIONS

Use Cases

• Students applying to jobs via AI-powered resume tailoring and one-click applications.
• Alumni providing mentorship, referrals, or startup funding to students.
• Recruiters sourcing verified candidates directly from universities.
• Universities tracking placement data, alumni engagement, and career outcomes.

Feasibility

• Technical: Leverages Expo for cross-platform mobile development, Node.js backend, and AI/ML (GPT-4o/Llama, LangChain) for resume tailoring and job matching.
• Market: Rising demand for AI-driven job search, career guidance and alumni engagement among students and graduates.
• Economic: Cloud-based infra (AWS Amplify, Supabase) keeps costs low while scaling efficiently.

Business Potential

• Subscription model for students (resume enhancement, job insights).
• Transaction fees for job postings and premium alumni mentorship.
• Partnerships with employers and recruitment agencies.
• Data analytics services for universities and corporates.

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Viability

• Large potential user base: Millions of students, job seekers, and alumni worldwide.
• Scalable: Can expand from a single university/region to global markets.
• Long-term value: Continuous user engagement through alumni mentorship, AI-driven upskilling, and job matching.

References:

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• Job Platforms & AI Tools:
• LinkedIn Jobs: https://www.linkedin.com/jobs/
• Indeed: https://www.indeed.com/
• Glassdoor: https://www.glassdoor.com/
• Research & Best Practices:
• The Future of Recruiting with AI (SHRM Report)
• AI and Automation in Job Applications (Forbes, 2023)
• Mobile Apps for Career Development (NACE Research)
• Feasibility Facts:
• LinkedIn Study (2022): 85% of jobs are filled via networking or referrals
• NASSCOM 2023 Report: AI-based recruitment platforms improve efficiency by up to 40%.
• McKinsey Report (2021): Automation can reduce repetitive recruitment tasks by 60–70%.