The Bloomberg Terminal for career decisions. PathIQ helps undergraduates compare post-graduation paths using AI analysis and live market data.

• Career Dashboard — Browse and filter 35 career paths with real BLS/O*NET data
• Market Insights — Aggregate charts: growth rates, salary rankings, category breakdown
• Multi-Path Comparison — Side-by-side comparison with salary trajectories and AI analysis (GPT-4o)
• AI Career Chat — Natural language exploration with database-backed answers (GPT-4o-mini)
• Career Detail Pages — Historical salary/employment charts (10 years), skills radar, AI-enhanced descriptions
• Onboarding — Personalized experience based on your major, year, and interests

• Framework: Next.js 16 (App Router) + TypeScript
• Styling: Tailwind CSS + shadcn/ui
• Database: Supabase (Postgres)
• AI: OpenAI GPT-4o (comparison) + GPT-4o-mini (chat, content generation)
• Data: BLS OEWS, BLS Projections, BLS Historical OES, O*NET Database
• Charts: Recharts
• Deploy: Vercel

npm install

cp .env.example .env.local

Fill in your keys:

Fresh setup: Run supabase/schema.sql in your Supabase SQL editor.

Existing database: Run supabase/migration_001_historical_and_ai.sql to add historical data and AI content columns.

cd data
pip install -r requirements.txt
cp .env.example .env

Edit data/.env with your credentials, then:

python collect_all.py

This will:

• Fetch salary/employment data from BLS OEWS
• Fetch growth projections from BLS Projections
• Fetch skills/interests from O*NET
• Fetch historical employment/wage data (2014–2024) from BLS
• Optionally fetch job counts from Adzuna, tech comp from levels.fyi, layoff data
• Validate all data (range checks, cross-field checks, known-range spot checks)
• Seed careers and market_trends tables in Supabase

Requires OPENAI_API_KEY in data/.env. Uses GPT-4o-mini (~$0.10 total for 35 careers).

python generate_ai_content.py

This generates for each career:

• An engaging description (~150 words)
• A career trajectory narrative (~150 words)
• Preparation advice for students (~150 words)

Results are cached in the database. Use --force to regenerate.

npm run dev

Open http://localhost:3000.

The pipeline includes automatic validation (data/validate_data.py) that checks:

• Range checks: salary, growth rate, employment within expected bounds
• Cross-field checks: entry salary ≤ median, salary trajectory is monotonically increasing
• Spot checks: known salary ranges for key careers (SWE, RN, physician, teacher, etc.)
• Warnings are printed to console but don't block seeding

cd data

# Fetch only BLS data
python fetch_bls.py

# Fetch only O*NET data
python fetch_onet.py

# Fetch only historical trends
python fetch_bls_history.py

# Validate existing exported data
python validate_data.py

# Generate AI content (requires OPENAI_API_KEY)
python generate_ai_content.py         # skip already-generated careers
python generate_ai_content.py --force  # regenerate all

# Seed Supabase from exported JSON
python seed_supabase.py

# Run full pipeline
python collect_all.py

src/
├── app/
│   ├── dashboard/page.tsx     # Career grid with filters
│   ├── insights/page.tsx      # Market overview charts + aggregate stats
│   ├── careers/[id]/page.tsx  # Career detail with historical charts
│   ├── compare/page.tsx       # Multi-path comparison
│   ├── chat/page.tsx          # AI career chat
│   ├── onboarding/page.tsx    # User profile setup
│   └── api/
│       ├── careers/           # Career CRUD endpoints
│       ├── market-trends/     # Historical trend data endpoint
│       ├── compare/           # AI comparison endpoint
│       └── chat/              # AI chat endpoint
├── components/                # UI components (charts, cards, filters)
├── hooks/                     # React hooks (useCareers, useMarketTrends)
└── lib/                       # Types, utils, Supabase client

data/
├── collect_all.py             # Master pipeline orchestrator
├── fetch_bls.py               # BLS OEWS + Projections fetcher
├── fetch_bls_history.py       # BLS historical time-series (2014–2024)
├── fetch_onet.py              # O*NET database parser
├── fetch_job_openings.py      # Adzuna API job counts
├── scrape_levels.py           # levels.fyi tech compensation
├── scrape_layoffs.py          # layoffs.fyi risk data
├── validate_data.py           # Data validation checks
├── generate_ai_content.py     # GPT-4o-mini content generation
├── seed_supabase.py           # Database seeder
├── career_mapping.json        # Master career → SOC code mapping
└── requirements.txt           # Python dependencies

supabase/
├── schema.sql                 # Full database schema (fresh setup)
└── migration_001_historical_and_ai.sql  # Incremental migration

vercel

Add environment variables in Vercel dashboard:

• OPENAI_API_KEY
• NEXT_PUBLIC_SUPABASE_URL
• NEXT_PUBLIC_SUPABASE_ANON_KEY
• SUPABASE_SERVICE_ROLE_KEY

Built for ProdCon 2026.