# Building Production-Grade Enterprise Search at Glean ## Company Overview Glean is an enterprise search company founded in 2019 by ex-Google engineers to solve the challenge of finding information across enterprise applications. Taking inspiration from Google's internal MOMA tool, they built a comprehensive search solution that has achieved unicorn status ($1B+ valuation) and serves major enterprises like Databricks, Canva, Confluent, and Duolingo. ## Technical Architecture and Approach ### Hybrid Search Implementation - Combines multiple search approaches rather than relying on a single technique: ### Core Search Components - Query understanding pipeline to interpret user intent - Document understanding to assess quality and relevance - Ranking components stack with multiple specialized modules - Personalization engine that considers: ### Infrastructure Considerations - Uses distributed elastic search as foundational infrastructure - Built integrations with major SaaS application APIs - Handles complex permission models across applications - Manages real-time indexing and freshness requirements ## Production Challenges and Solutions ### Search Quality - Rigorous tuning of ranking algorithms through iterative optimization - Focus on intellectual honesty in evaluating search quality - Regular evaluation of ranking components performance - Careful balance between different ranking signals ### Cost and Performance - Optimization of infrastructure costs while maintaining quality - Management of latency requirements for real-time search - Efficient handling of cross-application data access - Balancing index freshness with performance ### Enterprise Integration - Built robust API integrations with enterprise SaaS tools - Handle complex permission models across applications - Support for enterprise-specific features like Go links - Cross-platform mention tracking and notifications ## LLM Integration Strategy ### Thoughtful AI Adoption - Careful integration of LLMs rather than wholesale replacement - Recognition that LLMs are not silver bullets for all search problems - Hybrid approach combining traditional IR with modern AI - Focus on measurable user experience improvements ### AI Feature Development - Experimentation with chat interfaces - Testing of various LLM applications - Validation of AI features against user needs - Careful evaluation of cost-benefit tradeoffs ## Production System Features ### Core Enterprise Features - Cross-application search - Permission-aware results - Document collections - Go links functionality - Trending content identification - Feed recommendations - Mentions aggregation across platforms - Real-time updates ### Personalization Capabilities - Role-based customization - Team-aware results - Historical interaction incorporation - Organizational context understanding ## Deployment Considerations ### Enterprise Requirements - Security and compliance handling - Performance at enterprise scale - Integration with existing tools - Support for multiple data sources - Real-time indexing capabilities ### Operational Focus - Regular ranking algorithm updates - Performance monitoring and optimization - Cost management and efficiency - Integration maintenance and updates ## Lessons Learned ### Technical Insights - Importance of hybrid approaches over pure AI solutions - Value of rigorous ranking algorithm tuning - Need for personalization in enterprise context - Balance between features and performance ### Product Development - Focus on solving real user problems - Importance of retention-driving features - Need for integrated platform approach - Value of enterprise-specific features ### AI Integration - Careful evaluation of AI applications - Focus on measurable improvements - Recognition of AI limitations - Importance of traditional IR techniques ## Future Directions ### Technology Evolution - Continued experimentation with LLMs - Enhanced personalization capabilities - Improved cross-application integration - Advanced ranking algorithms ### Enterprise Features - Enhanced collaboration tools - Improved document understanding - Better context awareness - Advanced permission handling ## Implementation Guidelines ### Best Practices - Start with solid IR foundations