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Create a robust content enrichment pipeline that improves data quality through multiple AI and scraping services

Developers building content aggregation or curation systems

1-2 weeks for MVP implementation

What Success Looks Like

Consistent high-quality content metadata with 95%+ enrichment success rate and intelligent fallback handling

Steps to Execute

1

Set up multiple content acquisition services (RSS, Firecrawl, iFramely)

2

Implement Trigger.dev or similar for reliable job orchestration

3

Create quality scoring logic for each content type

4

Add AI model as intelligent fallback (Gemini with search)

5

Store winning content version with provenance tracking

6

Add retry logic and error monitoring

7

Create vector embeddings for content clustering

Checklist

Multiple data sources configured and tested
Quality scoring criteria defined for titles, descriptions, content
Fallback chain implemented (primary -> secondary -> AI)
Job orchestration handles failures gracefully
Database schema includes winner tracking and metadata
Monitoring alerts for high failure rates
Vector embedding generation working

Inputs Needed

  • List of RSS feeds or content sources
  • API keys for Firecrawl, iFramely, AI models
  • Trigger.dev or similar orchestration service
  • PostgreSQL with vector extensions
  • Quality criteria definitions for your content type

Outputs

  • Clean, standardized content database
  • Quality metrics and source performance tracking
  • Vector embeddings ready for similarity search
  • Automated enrichment pipeline running 24/7

Example

“Tech news aggregator processes 2000+ articles daily, automatically enriches with best available metadata, handles site blocking gracefully through AI fallback”