This AI Hiring Suite is basically a full-stack, AI-driven, end-to-end recruitment automation platform with real-time interview capability, calendar scheduling, assessments, fairness checks, and a proper modern dev stack.

- apps/web: Frontend (React, Tailwind, Vite)
- apps/api: Backend (Node, Express, TypeScript)
- packages/shared: Shared types
- scripts: Seed jobs
- postman: API requests
- infra: Infrastructure

1. Clone repository:

   git clone cd ai-hiring-suite

2. Install dependencies:

   npm install cd apps/web && npm install cd ../api && npm install

3. Setup environment:

   Copy .env.example → .env

   Fill in OpenAI, Cohere, Airtable, SMTP, Supabase keys.

4. Run locally (frontend + backend): npm run dev

5. Seed Jobs: cd scripts npm run ts-node seed_jobs.ts

6. Docker docker-compose up --build

7.Features

Resume parsing & candidate profile extraction

AI-generated interview questions & evaluation

Video/audio interview recording + STT transcription

Calendar scheduling & email notifications

Vector search & reranking for job matching

Admin dashboard & analytics

8. Environment Variables

   OPENAI_API_KEY

   COHERE_API_KEY

   AIRTABLE_API_KEY

   AIRTABLE_BASE_ID

   SMTP_HOST, SMTP_PORT, SMTP_USER, SMTP_PASS

   SUPABASE_URL, SUPABASE_KEY

   DATABASE_URL

9. Verification Steps

   Run ./run_all.sh to verify Docker, frontend, backend, and DB.

   Run ./full_check.sh to test resume upload, AI chains, interview flow, and calendar/email.

   Use Postman collection for API tests.

10. Deployment

    Use Docker + cloud provider (AWS, GCP, Azure, Railway, Supabase)

    Set all .env variables in cloud

    Expose frontend (5173) & backend (4000) ports

11. Infra Folder

    Use this folder to add:

    • Terraform scripts
    • AWS S3 bucket provisioning
    • Supabase instance setup
    • IAM roles and policies
    • Any other infrastructure automation