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A platform to automate the hiring process for startups using AI.

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HR Autopilot

AI-Powered Candidate Evaluation & Hiring Pipeline

A modern, full-stack application that automates the candidate screening process using AI. Upload resumes, parse job descriptions, and get detailed AI-powered evaluations with match scores, gap analysis, and interview recommendations. Supports RAG (Retrieval-Augmented Generation) via Pinecone for evidence-based CV parsing and candidate chat.

Table of Contents

Features

Feature Description
Job Blueprint Generation AI parses job descriptions into structured blueprints with must-have/nice-to-have requirements
CV Parsing Automatic extraction of skills, experience, education from PDF resumes
AI Evaluation Deep candidate-to-job matching with confidence scores and evidence-based reasoning
Gap Analysis Critical, major, and moderate gaps identified with transferability assessment
Email Generation Auto-generated invite/reject/hold emails based on evaluation
Candidate Chat AI-powered Q&A about specific candidates
Multi-tenant Organization-based data isolation with JWT authentication

Architecture

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│                 │     │                 │     │                 │
│    Frontend     │────▶│    Backend      │────▶│   PostgreSQL    │
│   (Next.js)     │     │   (Hono/Bun)    │     │                 │
│   Port: 3000    │     │   Port: 8000    │     │   Port: 5432    │
│                 │     │                 │     │                 │
└─────────────────┘     └────────┬────────┘     └─────────────────┘
                                 │
                    ┌────────────┼────────────┬────────────┐
                    │            │            │            │
                    ▼            ▼            ▼            ▼
             ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐
             │  Redis   │ │   LLM    │ │ Pinecone │ │  Email   │
             │  Cache   │ │ Provider │ │ (Vector) │ │  (SMTP)  │
             └──────────┘ └──────────┘ └──────────┘ └──────────┘

Data Flow

  1. Job Creation: JD text → LLM → Structured Blueprint (skills, experience, responsibilities)
  2. CV Upload: PDF → Parser → Chunking → Embeddings → Pinecone; LLM (with optional RAG) → Candidate Profile
  3. Evaluation: Blueprint + Profile + CV Text (and optionally RAG chunks) → LLM → Detailed Evaluation with scores
  4. Candidate Chat: Question + Profile + Job Blueprint + RAG chunks → LLM → Answer (optionally streamed)
  5. Decision: HR reviews evaluation → Makes decision → Auto-generates appropriate email

Tech Stack

Backend

Technology Purpose
Bun JavaScript runtime (fast, TypeScript-native)
Hono Lightweight web framework
Drizzle ORM Type-safe database queries
PostgreSQL Primary database
Redis Caching layer

Frontend

Technology Purpose
Next.js 14 React framework with App Router
TailwindCSS Utility-first styling
shadcn/ui Component library
TanStack Query Data fetching & caching

AI/LLM

Provider Models Supported
Gemini gemini-2.0-flash (default)
OpenAI gpt-4o, gpt-4-turbo
Anthropic claude-3-5-sonnet

Quick Start

Using Docker (Recommended)

# 1. Clone and enter directory
cd HR

# 2. Create backend environment file
cat > backend/.env << 'EOF'
GEMINI_API_KEY=your_gemini_api_key
LLM_PROVIDER=gemini
JWT_SECRET=your-secret-key-min-32-chars-long
EOF

# 3. Start all services
docker compose up --build

# 4. Access the application
# Frontend: http://localhost:3000
# Backend:  http://localhost:8000

Local Development

# Backend
cd backend
# Create backend/.env with at least LLM_PROVIDER, API key, JWT_SECRET, DATABASE_URL
bun install
bun run db:migrate     # Initialize database schema
bun run dev             # Start dev server on :8000

# Frontend (separate terminal)
cd frontend
# Create frontend/.env.local with NEXT_PUBLIC_API_URL=http://localhost:8000
npm install
npm run dev             # Start dev server on :3000

Optional (RAG): To enable CV chunking and candidate chat with citations, set PINECONE_API_KEY and PINECONE_INDEX_NAME in backend/.env, and create a Pinecone index with dimension 512 and metric cosine.

Configuration

Backend environment variables

Create backend/.env. You can copy backend/.env.example and fill in values.

Variable Required Description
LLM_PROVIDER Yes gemini, openai, or anthropic
GEMINI_API_KEY If Gemini Google AI API key
OPENAI_API_KEY If OpenAI OpenAI API key
ANTHROPIC_API_KEY If Anthropic Anthropic API key
JWT_SECRET Yes Secret for signing JWTs (min 32 chars in production)
DATABASE_URL Yes* PostgreSQL connection string (*set automatically in Docker)
REDIS_URL No Redis URL (default: redis://localhost:6379/0)
PORT No Server port (default: 8000)
API_PREFIX No API path prefix (default: /api)
CORS_ORIGINS No Comma-separated allowed origins
JWT_EXPIRES_IN_SECONDS No Token expiry (default: 604800 = 7 days)
GEMINI_MODEL No Gemini model (default: gemini-2.0-flash)
OPENAI_MODEL No OpenAI model (default: gpt-4-turbo-preview)
ANTHROPIC_MODEL No Anthropic model (default: claude-3-opus-20240229)
Pinecone (RAG)
PINECONE_API_KEY For RAG Enables RAG CV parsing and candidate chat with citations
PINECONE_INDEX_NAME No Index name (default: cv-chunks)
PINECONE_ENVIRONMENT No e.g. us-east-1
EMBEDDING_MODEL No e.g. text-embedding-004 (Gemini) or text-embedding-3-small (OpenAI)
EMBEDDING_DIMENSION No Must match index (default: 512)
Email
EMAIL_HOST No SMTP host (e.g. smtp.gmail.com)
EMAIL_PORT No SMTP port (e.g. 587)
EMAIL_USER No SMTP username
EMAIL_PASS No SMTP password / app password
EMAIL_FROM No From address for outgoing emails
HIRING_MANAGER No Display name (default: Hiring Manager)
Other
ENVIRONMENT No development or production
LOG_LEVEL No e.g. INFO, DEBUG

Frontend environment variables

Create frontend/.env.local:

Variable Required Description
NEXT_PUBLIC_API_URL Yes Backend API base URL (e.g. http://localhost:8000)

For Docker, the frontend receives the API URL via build args; for local dev, set NEXT_PUBLIC_API_URL in .env.local.

API Reference

Authentication

Endpoint Method Description
/api/auth/signup POST Create new user account
/api/auth/login POST Get JWT token
/api/auth/me GET Get current user

Jobs

Endpoint Method Description
/api/jobs GET List all jobs
/api/jobs POST Create job (with JD parsing)
/api/jobs/:id GET Get job details
/api/jobs/:id DELETE Delete job

Candidates

Endpoint Method Description
/api/jobs/:id/candidates GET List candidates for job
/api/jobs/:id/candidates POST Upload CV (multipart/form-data)
/api/candidates/:id GET Get candidate details
/api/candidates/:id/evaluate POST Trigger AI evaluation
/api/candidates/:id/evaluation GET Get evaluation results
/api/candidates/:id/chat POST Chat about candidate

Evaluations

Endpoint Method Description
/api/evaluations/:id/decision PATCH Set final decision
/api/evaluations/:id/email-draft POST Generate email
/api/evaluations/:id/send-email POST Send email to candidate

Candidate chat (RAG-aware)

Endpoint Method Description
/api/candidates/:candidateId/chat POST Send message, get answer (body: { question, conversation_history? })
/api/candidates/:candidateId/chat/stream POST Stream answer via SSE (same body)
/api/candidates/:candidateId/chat/suggestions GET Get suggested questions for the candidate

Audit

Endpoint Method Description
/api/candidates/:candidateId/timeline GET Audit timeline for a candidate
/api/jobs/:jobId/audit-logs GET Audit logs for a job

Project Structure

HR/
├── backend/
│   ├── src/
│   │   ├── index.ts           # App entry point
│   │   ├── config.ts          # Environment configuration
│   │   ├── database.ts        # Drizzle DB connection
│   │   ├── models/            # Database schemas (Drizzle)
│   │   ├── routes/            # API route handlers
│   │   ├── services/          # Business logic
│   │   │   ├── llmClient.ts       # LLM integration
│   │   │   ├── evaluationService.ts
│   │   │   ├── candidateService.ts
│   │   │   └── jobService.ts
│   │   ├── prompts/           # LLM prompt templates
│   │   │   └── v1/            # Versioned prompts
│   │   └── middleware/        # Auth middleware
│   └── package.json
│
├── frontend/
│   ├── src/
│   │   ├── app/               # Next.js App Router pages
│   │   │   ├── jobs/          # Job management
│   │   │   ├── candidates/    # Candidate views
│   │   │   └── settings/      # App settings
│   │   ├── components/        # React components
│   │   │   ├── ui/            # shadcn components
│   │   │   └── *.tsx          # Feature components
│   │   ├── lib/               # Utilities
│   │   └── types/             # TypeScript types
│   └── package.json
│
├── docker-compose.yml         # Container orchestration
└── README.md

Evaluation Output Structure

The AI evaluation produces a comprehensive assessment:

{
  decision: "yes" | "maybe" | "no",
  confidence: 0.85,
  overall_match_score: 0.72,
  
  // Detailed Analysis
  jd_requirements_analysis: { must_have: [...], nice_to_have: [...] },
  skills_comparison: [{ skill, jd_requirement, candidate_level, matches, evidence }],
  experience_analysis: { jd_requirement, candidate_years, matches, gap_analysis },
  
  // Gap Analysis
  matching_strengths: { skills_that_match: [...], experience_that_matches: [...] },
  missing_gaps: { technology_gaps, experience_gaps, skill_gaps },
  brutal_gap_analysis: { critical_gaps, major_gaps, moderate_gaps },
  
  // Recommendations
  strengths: [{ point, evidence }],
  concerns: [{ point, evidence }],
  recommended_interview_questions: [...]
}

Troubleshooting

Issue What to check
"OPENAI_API_KEY / GEMINI_API_KEY not set" Set the API key for your chosen LLM_PROVIDER in backend/.env.
"PINECONE_API_KEY is required" RAG features (CV chunk storage, candidate chat with citations) need Pinecone. Either set PINECONE_API_KEY and create an index with the same dimension as EMBEDDING_DIMENSION (default 512), or ensure the code path does not require Pinecone.
Pinecone index not found Create the index in the Pinecone console with dimension 512 (or your EMBEDDING_DIMENSION), metric cosine. Name must match PINECONE_INDEX_NAME.
CV parsing fails or returns "Error" Check PDF is valid and not scanned/image-only. Check LLM API key and rate limits.
Email not sending Set EMAIL_HOST, EMAIL_PORT, EMAIL_USER, EMAIL_PASS. For Gmail, use an App Password.
CORS errors in browser Add your frontend origin to CORS_ORIGINS in backend/.env (comma-separated).
401 on API calls Ensure the frontend sends Authorization: Bearer <token> and the token is valid. Log in again if expired.

Contributing

See CONTRIBUTING.md for development setup, code style, and how to submit changes.

Documentation

Document Description
README.md This file – overview, setup, API, config
CONTRIBUTING.md How to run locally, code style, PR process
backend/README.md Backend scripts, env, directory structure
frontend/README.md Frontend scripts, env, API usage

Key backend modules are documented with JSDoc: config.ts, database.ts, index.ts, middleware/auth.ts, prompts/registry.ts, and services under backend/src/services/ (e.g. llmClient.ts, candidateService.ts, evaluationService.ts, vectorStoreService.ts, embeddingService.ts). Frontend: lib/api.ts, contexts/auth-context.tsx.

License

MIT

Built with AI, for HR teams who value their time.

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A platform to automate the hiring process for startups using AI.

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