Career Genie — AI Career & Scholarship Intelligence Platform

Executive Summary

Career Genie was engineered to solve a structural gap in career counseling: most students receive generic advice that is not aligned with evolving job markets or verified scholarship opportunities.

The objective was to build a scalable AI-driven SaaS platform capable of:

• Generating personalized career paths
• Matching users with relevant scholarships
• Identifying skill gaps
• Providing resume feedback
• Delivering real-time conversational guidance

The system was designed using a Retrieval-Augmented Generation (RAG) architecture to ensure outputs are grounded in structured datasets rather than hallucinated responses.

---

The Problem

Traditional career counseling suffers from:

• Static and outdated guidance
• Fragmented scholarship information
• Lack of personalization
• No explainability in recommendations
• Limited access for students in emerging markets

Most platforms either:

• Provide assessments only
• Offer scholarship listings only
• Or rely purely on LLM chat without grounded data

There was no unified intelligence layer combining career data, scholarships, and AI reasoning into a single system.

---

The Solution

Career Genie integrates:

1. Structured career datasets (O*NET-based)
2. Verified scholarship data (government + international sources)
3. Semantic search via vector embeddings
4. LLM reasoning for explanation and personalization

The result is a contextual, explainable AI system that delivers:

• 3–5 best-fit career paths
• Eligibility-matched scholarships
• Course recommendations based on skill gaps
• Resume analysis aligned to selected career paths
• A real-time AI chatbot grounded in retrieved knowledge

---

System Architecture

1. Data Layer

• Career dataset collected and normalized from O*NET
• Scholarship dataset curated via scraping and manual validation
• Structured storage in PostgreSQL
• Embedding generation using gemini-embedding-001

2. Retrieval Layer

• Vector embeddings stored in Supabase (pgvector)
• Semantic similarity search for:
  • Career alignment
  • Scholarship eligibility
  • Skill-to-role mapping

3. Intelligence Layer (RAG)

• Retrieved context injected into prompts
• Gemini 2.5 Flash generates:
  • Personalized recommendations
  • Resume feedback
  • Assessment explanation
  • Chat responses

This prevents hallucinations and ensures grounded outputs.

4. Application Layer

• FastAPI backend for orchestration
• Next.js frontend for dashboards and onboarding
• Streaming chatbot interface (SSE-based)
• Role-based views for students and institutes

---

Engineering Decisions

Why RAG Instead of Pure LLM?

To avoid hallucinated career or scholarship data and ensure explainable outputs grounded in verified datasets.

Why Gemini 2.5 Flash?

Balanced reasoning capability and latency efficiency for interactive applications.

Why Supabase + PostgreSQL?

• Native pgvector support
• Structured + vector hybrid queries
• Rapid deployment and scalability

---

Performance Metrics

• Career recommendation generation: 20–30 seconds
• Resume analysis: 10–15 seconds
• Chatbot response latency: 3–5 seconds
• Output relevance: ~80–85% grounded alignment
• Consistency score: ~85–90%
• Functional testing: 15/15 test cases passed

---

Scalability Strategy

• Modular RAG pipeline for model upgrades
• Dataset expansion capability (multi-country support)
• Multi-tenant architecture readiness
• Future monetization via subscription tiers

---

Outcome

Career Genie demonstrates how structured labor-market data combined with RAG-based AI systems can transform career counseling from static advice into dynamic intelligence.

It is architected as a production-ready SaaS platform with extensible data pipelines and scalable AI orchestration.