TalentIQ - AI Recruitment & Career Intelligence Platform

TalentIQ is a hybrid PHP + Python platform for candidate screening, recruiter decision support, and career intelligence workflows powered by ML models.

Problem Statement

Recruitment pipelines are often slow, inconsistent, and biased due to manual screening and fragmented evaluation criteria. Teams need data-driven assistance for matching, interviews, and hiring decisions.

Solution Overview

TalentIQ combines:

• role-specific dashboards (admin/recruiter/candidate)
• AI-assisted resume and candidate analysis
• hiring pipeline intelligence and simulation
• model-backed features for fit scoring and interview support

Architecture

graph LR
    U[Users by Role] --> UI[PHP Web Portal]
    UI --> APP[PHP Application Layer]
    APP --> DB[(MySQL)]
    APP --> AIS[Python AI Service]
    AIS --> M[(ML Models)]

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Tech Stack

• Frontend: PHP-rendered dashboards + assets
• Backend: PHP modules + Python AI API
• Database: MySQL via PDO
• AI/ML: TensorFlow, scikit-learn, joblib-based models
• Runtime: XAMPP + Python environment

Features

• Role-based recruitment workflows
• Resume analysis and candidate scoring
• Interview support and prediction paths
• Recruiter/admin analytics and decision support
• Candidate career intelligence flows

Project Structure

TalentIQ/
  index.php
  config.php
  includes/
    database.php
    ai_handler.php
    auth_class.php
  auth/
  dashboard/
    admin.php
    recruiter.php
    candidate.php
  api/
    ai_server.py
    requirements.txt
  ai/
    admin/
    recruiter/
    candidate/
  docs/
    schema.sql
    DEPLOYMENT.md
    API.md
    SECURITY.md
  tests/
    README.md

Screenshots

Add screenshots in docs/screenshots/:

• home.png
• recruiter-dashboard.png
• candidate-dashboard.png
• admin-dashboard.png

Demo

• Live URL: https://<your-demo-url>
• Demo GIF: docs/screenshots/demo.gif

Installation

1. Clone repository.
2. Copy .env.example to .env and configure values.
3. Import docs/schema.sql.
4. Create Python virtual environment and install api/requirements.txt.
5. Run Python AI server.
6. Start Apache/MySQL and open http://localhost/TalentIQ.

Usage

1. Authenticate as recruiter/candidate/admin.
2. Use role dashboards for workflow tasks.
3. Trigger AI-assisted evaluations and review outputs.

API Documentation

See docs/API.md for Python AI service integration notes.

Deployment Plan

Recommended split deployment:

• PHP app: Render or Railway
• AI service: separate Python service (Render/Railway)
• DB: managed MySQL

Steps: docs/DEPLOYMENT.md

Security Notes

See docs/SECURITY.md for model/data handling and role-access hardening guidance.

Future Improvements

• Add model versioning and inference audit trail.
• Add unified API contract + schema validation.
• Add robust bias/fairness monitoring dashboards.
• Add integration tests across PHP↔Python boundaries.

Contributing

See CONTRIBUTING.md.

License

MIT License - see LICENSE.