Enterprise search is hard: why it’s so behind—and what it’ll take to catch up

This post elaborates on the challenges and requirements for building effective enterprise search, emphasizing the need for semantic understanding, company-specific language models, knowledge graphs, and deep personalization. It highlights how Glean's approach, including its autocomplete system, addresses these challenges by leveraging custom deep learning models for semantic search and synonym understanding, building a knowledge graph of company activity, and deeply personalizing search results based on user roles, documents, and tools. The post also touches upon the importance of robust ranking algorithms that learn from feedback without overfitting and the need for a unified interface across all applications.