DiscoverAI - Intelligent Content Curation Platform

This is a submission for the Storyblok Challenge

What I Built

DiscoverAI is an intelligent content curation platform that combines Storyblok's content management with advanced AI to help users discover, organize, and share relevant content across multiple domains. It serves content creators, researchers, and knowledge workers with smart content discovery and organization tools.

The platform uses AI to analyze content stored in Storyblok, identify patterns and relationships, and provide personalized recommendations while maintaining human editorial control.

Demo

Storyblok Space: https://app.storyblok.com/#!/me/spaces/567894/stories

Code Repository: https://github.com/devuser/discoverai-platform
Licensed under MIT License

Demo Video or Screenshots

Tech Stack

• Frontend: Vue.js 3, Nuxt 3, Tailwind CSS
• Backend: Python, FastAPI, PostgreSQL
• AI: OpenAI GPT-4, Pinecone Vector DB, LangChain
• CMS: Storyblok
• Search: Elasticsearch
• Deployment: Google Cloud Platform

How I Used Storyblok

Storyblok serves as the content repository and management system:

1. Content Collections: Organized repositories of articles, research papers, and multimedia content
2. Topic Taxonomies: Hierarchical category systems with AI-generated tags
3. Source Management: Publication and author information with credibility scoring
4. Curation Rules: Content filtering and quality guidelines
5. User Profiles: Personalization preferences and interest mapping
6. Editorial Workflows: Review and approval processes for curated content

The Visual Editor enables content curators to organize and present discovered content in engaging, themed collections.

AI Integration

This submission competes for the Amazing AI category with comprehensive AI features:

1. Intelligent Content Analysis: AI extracts key themes, entities, and concepts from Storyblok content
2. Semantic Search: Vector-based search that understands context and meaning
3. Personalized Recommendations: AI learns user preferences to suggest relevant content
4. Content Summarization: Automatic generation of article summaries and key points
5. Trend Detection: AI identifies emerging topics and content patterns
6. Quality Scoring: Automated content quality assessment based on multiple factors
7. Smart Tagging: AI generates relevant tags and categories for content organization

Learnings and Takeaways

Integrating AI with Storyblok created a powerful content intelligence system. The structured content in Storyblok provided rich metadata that enhanced AI analysis and recommendation accuracy.

The most challenging aspect was balancing AI automation with human editorial judgment. I implemented confidence scoring systems that flag content for human review when AI certainty falls below established thresholds.

Storyblok's webhook system enabled real-time AI processing of new content, allowing the platform to continuously learn and improve recommendations.

The project demonstrated how AI can enhance content discovery and organization while preserving the editorial expertise that makes content curation valuable.

Future developments will include collaborative filtering algorithms and integration with academic databases for specialized research content discovery.