Linkter AI: Semantic internal linking

Linkter AI Interface

Overview

As lead developer of Linkter AI, I built a complete SaaS solution zero -> one which solved the problem of internal linking. At this point it works only with WordPress websites, but we aim of supporting different CMS types such as Webflow, Shopify, Framer and others.

Technical Achievements

Cloud Architecture

  • Designed and implemented scalable AWS infrastructure using Lambda, Step Functions, and EC2
  • Built efficient data processing pipeline handling sites up to 5,000 pages
  • Implemented cost-effective parallel processing for multi-site content analysis
  • Deployed sophisticated ML models via custom EC2 configurations

Backend Development

  • Created Python/Flask microservices architecture
  • Implemented secure data handling with Fernet encryption
  • Built robust WordPress integration system
  • Designed efficient PostgreSQL/DynamoDB hybrid storage solution

Frontend Implementation

  • Developed intuitive React/Redux interface reducing cognitive load
  • Created custom components for specialized content analysis
  • Built real-time preview and suggestion system
  • Implemented responsive design supporting all devices

WordPress Integration

  • Developed lightweight plugin supporting complex page builders
  • Implemented secure authentication via WP application passwords
  • Created automated content synchronization
  • Built robust error handling and recovery systems

Business Impact

  • Increased user engagement through improved content discovery
  • Enhanced ad revenue via better internal navigation
  • Improved search rankings for client target keywords
  • Significantly reduced manual linking workload

Linkter AI Interface

Technical Stack

  • Frontend: React, Redux, TypeScript
  • Backend: Python, Flask, SQLAlchemy
  • Infrastructure: AWS Lambda, Step Functions, RDS, DynamoDB, EC2
  • DevOps: Terraform, GitHub Actions
  • Design: Figma

Key Features

  • AI-powered content analysis
  • Three-tier matching system (Exact, Partial, Contextual)
  • Real-time link suggestions
  • SEO optimization safeguards
  • Visual relationship mapping
  • Automated content processing
  • Secure credential management
  • Performance monitoring
  • Scalable architecture

Keyword semantic proximity

Development Process

Led end-to-end development from concept to deployment:

  1. Created initial design and wireframes
  2. Built MVP core functionality
  3. Implemented cloud infrastructure
  4. Developed WordPress integration
  5. Deployed monitoring and scaling systems
  6. Continuously improved based on user feedback

Results

The platform successfully processes thousands of pages across multiple websites, helping content creators:

  • Discover relevant internal linking opportunities
  • Improve site structure and SEO
  • Increase page views and user engagement
  • Save significant time on content optimization

Keyword semantic proximity