TikTrack is a Next.js application developed to enhance the visibility of job opportunities in lesser-known or hard-to-access cities in Colombia by connecting Magneto recruiters with local TikTok influencers.
Magneto struggles to promote job listings effectively in remote areas due to:
- Low platform visibility in smaller cities.
- Difficulty identifying location-specific influencers.
TikTrack addresses these challenges by:
- Searching and classifying influencers by city and engagement metrics.
- Enabling automated outreach with AI-enhanced messages and templates.
- Ensuring influencer data quality through a supervised reporting system.
- Frontend Framework: Next.js
- Language: TypeScript / JavaScript
- Styles: Tailwind CSS
- API Communication: RESTful APIs
- Microservices:
fetch-influencers: Retrieves influencer data based on location or hashtags using Python and Selenium.send-messages: Sends AI-refined or template-based messages to influencers using Python and Selenium.
- Influencer search with advanced filtering by location, engagement, followers or last updated.
- Message drafting, sending and enhancement using AI.
- Reporting and deactivate of misclassified influencers.
- Role-based access (Recruiters and Supervisors).
- Influencer comparison feature.
- Influencers that are automatically added daily from the tiktok platform according to each city.
# Clone the repository
git clone https://github.com/your-org/tiktrack.git
cd tiktrack
# Install dependencies
npm install
# Define .env
DATABASE_URL=
OPENAI_API_KEY=
OPENAI_API_MODEL=
OPENAI_API_URL=
POSTGRES_USER=
POSTGRES_PASSWORD=
POSTGRES_DB=
NEXT_PUBLIC_BASE_URL_API=
NEXT_PUBLIC_FETCH_INFLUENCERS_MICROSERVICE_URL=
NEXT_PUBLIC_SEND_MESSAGE_MICROSERVICE_URL=
# Start development server
npm run dev
# Access the app
http://localhost:3000