YouTube Influencer Analytics & Automation Platform

This project is a full-stack web application designed to analyze YouTube influencer data at scale and automate content engagement workflows. The platform enables users to research influencers by topic, analyze video performance, and generate AI-powered comments to increase engagement.

Built with a modern, containerized architecture, the system supports high-volume data processing, background task execution, and direct integrations with YouTube and email services, making it suitable for daily active usage by hundreds of users.

Analyzing large volumes of YouTube data and automating engagement actions presents several challenges:

Fetching and processing thousands of videos efficiently

Avoiding UI blocking during heavy API operations

Generating meaningful, context-aware comments at scale

Supporting concurrent users with reliable background processing

Ensuring easy deployment and environment consistency

The goal was to build a scalable, automated solution that could handle data-heavy operations while maintaining a smooth user experience.

Designed and implemented the backend using Django REST Framework

Built a responsive frontend with React.js for data visualization and workflows

Containerized the entire application using Docker

Implemented background processing with Celery for long-running tasks

Integrated LLMs for AI-powered comment generation

Connected YouTube API for large-scale data retrieval

Implemented email automation via Hostinger Mail Server

✅ YouTube Data Analysis – fetch and analyze up to 10,000 videos per topic ✅ Influencer Discovery – topic-based influencer research ✅ AI Comment Generation – LLM-powered, context-aware engagement comments ✅ Background Processing – Celery-powered async tasks for scalability ✅ Direct YouTube Posting – automated comment posting to YouTube ✅ Bulk Email Automation – outreach emails via Hostinger Mail Server ✅ User Management – supports up to 500 daily active users ✅ Dockerized Deployment – consistent environments across dev and production

Backend: Django REST Framework

Frontend: React.js

Language: Python

Async Processing: Celery

Containerization: Docker

Integrations: YouTube API, LLMs, Hostinger Mail Server

Successfully processed large-scale YouTube data with minimal latency

Enabled automated engagement workflows at scale

Supported up to 500 daily users without performance degradation

Improved deployment reliability with Docker-based environments

Reduced manual influencer engagement efforts through AI automation

The platform delivered a scalable, production-ready solution for influencer analytics and engagement automation. By combining Django, React, Docker, Celery, and AI-driven workflows, the system enables efficient data analysis and meaningful content interaction - helping users scale their outreach with minimal effort.