AI-powered job matching that analyzes your resume and surfaces roles that fit your trajectory — not just your keywords.

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Overview

PathAI is a full-stack job platform where candidates upload a resume and receive AI-ranked job matches. The matching engine combines GPT-4o-mini structured extraction with pgvector cosine similarity to surface roles that are genuinely relevant — correctly levelled by seniority, ranked by semantic fit.

Built as a portfolio project demonstrating vector search, AI-driven personalization, and a polished multi-page product experience.

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Features

Feature	Description
Resume Matching	Upload PDF/DOCX — GPT-4o-mini extracts your profile, pgvector ranks jobs by semantic similarity
Personalized Match Scores	Every job detail page computes your cosine similarity score on demand against your resume embedding
Apply Flow	External job links open the posting then prompt "Did you apply?" — confirmed applications land in your dashboard
ATS Dashboard	Track applications across stages: Applied → Phone Screen → Interview → Offer → Hired
Saved Jobs	Bookmark any job from the feed; saved roles appear in a dedicated dashboard section
Interview Prep	Role-specific interview questions generated per job
Company Logos	Clearbit logo API on all job cards with letter-initial fallback
Authentication	Supabase Auth with email/password, email confirmation flow, and protected routes

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How the Matching Works

Pure vector similarity has a well-known blind spot: a Junior Engineer and a VP of Engineering share nearly identical embeddings because the domain is the same. PathAI solves this with a two-layer approach.

Layer 1 — Seniority Pre-Filter

On resume upload, GPT-4o-mini extracts structured metadata:

{
  "seniority": "senior",
  "domain": "software_engineering",
  "skills": ["python", "react", "aws"],
  "years_experience": 8
}

Jobs are filtered to only include roles within a compatible seniority band before any vector math runs. A mid-level engineer never sees intern or VP postings.

Layer 2 — Vector Similarity

Each job stores a pre-computed pgvector embedding of its title and description. The resume text is embedded at upload time via text-embedding-3-small. Cosine distance is computed in-database and the top matches are returned from the already-filtered pool.

The result: semantically relevant roles, correctly levelled.

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Architecture

┌──────────────────────┐         ┌──────────────────────┐
│   Next.js 16         │ ──────► │   FastAPI Backend    │
│   (Vercel)           │ ◄────── │   (Railway)          │
└──────────────────────┘         └──────────┬───────────┘
                                            │
                                            ▼
                                  ┌──────────────────────┐
                                  │  Supabase            │
                                  │  PostgreSQL + pgvector│
                                  └──────────┬───────────┘
                                            │
                                            ▼
                                  ┌──────────────────────┐
                                  │  OpenAI API          │
                                  │  text-embedding-3-   │
                                  │  small + gpt-4o-mini │
                                  └──────────────────────┘

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Tech Stack

Frontend

	Library	Purpose
	Next.js 16	App Router, SSR, routing
	React 19	UI framework
	TypeScript	Type safety
	Tailwind CSS	Styling
	Framer Motion	Animations

Backend

	Library	Purpose
	FastAPI	REST API
	Python 3.14	Runtime
	SQLAlchemy 2	ORM

Data & AI

	Service	Purpose
	Supabase	PostgreSQL, Auth
	pgvector	Vector similarity search
	OpenAI	Embeddings + extraction

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Project Structure

PathAI/
├── frontend/                        # Next.js 16 — App Router
│   └── src/
│       ├── app/
│       │   ├── page.tsx             # Landing page
│       │   ├── jobs/page.tsx        # Job listings with filters + pagination
│       │   ├── jobs/[id]/page.tsx   # Job detail + AI Match Analysis widget
│       │   ├── resume/page.tsx      # Resume upload + profile analysis
│       │   ├── dashboard/page.tsx   # ATS dashboard, saved jobs, applications
│       │   ├── about/page.tsx       # About / how it works
│       │   └── auth/page.tsx        # Sign in / sign up
│       ├── components/
│       │   └── CompanyLogo.tsx      # Clearbit logo with letter-initial fallback
│       ├── context/
│       │   └── AuthContext.tsx      # Supabase session management
│       └── lib/
│           ├── api.ts               # All API calls (jobs, resume, match score, saved)
│           └── supabase.ts          # Supabase client
│
└── backend/                         # FastAPI (Python)
    ├── main.py                      # All routes + middleware
    ├── models.py                    # SQLAlchemy ORM models
    ├── schemas.py                   # Pydantic request / response schemas
    ├── auth.py                      # Supabase JWT verification
    ├── seed_data.py                 # Synthetic job generator
    └── services/
        ├── matching.py              # Dual-layer matching logic
        ├── resume_parser.py         # PDF/DOCX extraction + GPT-4o-mini parsing
        └── embedding.py             # OpenAI embeddings wrapper

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Setup

Prerequisites

• Python 3.11+
• Node.js 18+
• Supabase project (free tier works)
• OpenAI API key

Backend

cd backend
python -m venv venv && source venv/bin/activate
pip install -r requirements.txt
python seed_data.py        # Populate DB with synthetic jobs
uvicorn main:app --reload  # http://localhost:8000

backend/.env

SUPABASE_URL=
SUPABASE_SERVICE_KEY=
SUPABASE_JWT_SECRET=
OPENAI_API_KEY=
DATABASE_URL=

Frontend

cd frontend
npm install
npm run dev    # http://localhost:3000

frontend/.env.local

NEXT_PUBLIC_SUPABASE_URL=
NEXT_PUBLIC_SUPABASE_ANON_KEY=
NEXT_PUBLIC_API_URL=http://localhost:8000

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_{© 2026 PathAI}