Case Study: Frankfurter Allgemeine Zeitung unlocks 75 years of journalism with Qdrant

Frankfurter Allgemeine Zeitung unlocks 75 years of journalism with Qdrant and sub-second search

Frankfurter Allgemeine Zeitung (faz), a major German national newspaper, faced the challenge of making its vast 75-year journalism archive accessible beyond simple keyword matching. They needed a semantic search platform that could understand meaning, support complex metadata filters, and operate within their own Azure infrastructure. To address this, faz turned to vendor Qdrant to build a new AI-powered research engine.

The solution implemented using Qdrant's vector database involved embedding article paragraphs with Azure OpenAI and indexing them with over 60 metadata fields for rich filtering. Qdrant provided sub-second search performance at scale, handling real-time updates and daily ingestions. The results included a powerful semantic search system that transformed archival journalism into an intuitive research tool, with plans to scale to 40-50 million vectors and develop a hybrid search architecture for even greater precision.

Frankfurter Allgemeine Zeitung (faz)