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Enterprise Autocomplete System

7 posts · Nov 2025 – Aug 2026

What this trail covers

Glean's enterprise AI stack has been enhanced with an Autocomplete system designed to provide users with faster access to relevant information. This system suggests potential queries, documents, operators, and structured results (people, teams) with near-zero latency. It addresses the challenges of enterprise search by mining queries from indexed documents and developing low-latency models for document prediction, while respecting user permissions. The system aims to significantly reduce the average search time and improve user productivity. This post details the benchmarking of key metrics for evaluating enterprise search tools, focusing on accuracy, speed, user experience, and pricing. It highlights the importance of structured benchmarks for comparing search tool options against business goals, including tool calling accuracy, response times, interface usability, and cost efficiency. Specific metrics for accuracy include tool calling accuracy and context retention, with benchmarks targeting 90% or higher for both. Speed metrics focus on response times under 1.5 to 2.5 seconds and update frequency, emphasizing real-time or near-real-time indexing. User experience is evaluated through interface intuitiveness and reporting quality. The post also compares leading search tools like Glean, Microsoft Search, Elastic Enterprise Search, Coveo, and Sinequa, discussing their trade-offs. Finally, it emphasizes aligning search tool choice with business needs and use cases, considering pricing models and feature sets.

All Glean trails
  1. Enterprise search is hard: why it’s so behind—and what it’ll take to catch upThis 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.
  2. The unsolved problem of enterprise autocompleteThis post details the technical challenges and solutions for building a low-latency enterprise autocomplete system. It addresses the 'cold start problem' for query suggestions by mining queries from permission-aware, translated document content rather than relying on query volume. For document suggestions, it uses a low-latency model to predict user visits and filters/ranks based on keywords, acknowledging the difficulty of optimal ranking when multiple documents share similar keywords. The system aims to reduce average time to satisfaction by 40% and currently drives ~20% of result clicks.
  3. What is semantic search, and how does it enhance enterprise productivity?This post explains the fundamental principles of semantic search, which underpins Glean's enterprise autocomplete system. It details how semantic search understands user intent, maps concepts, and provides contextual relevance by leveraging NLP and ML algorithms. The post contrasts semantic search with traditional keyword search, highlighting its advantages in ambiguity resolution, synonym recognition, and language variation handling. It also provides workplace examples of semantic search applications and introduces Glean as an enterprise search platform that incorporates these advanced capabilities.
  4. 2025 search tool benchmark: key metrics to evaluate accuracy and speedThis post introduces a framework for benchmarking enterprise search tools, focusing on key metrics like accuracy, speed, user experience, and pricing. It defines specific metrics for accuracy (tool calling accuracy, context retention) and speed (response time, update frequency), providing industry benchmarks for 2025. It also discusses user experience evaluation and compares several leading enterprise search platforms, including Glean, highlighting their respective strengths and trade-offs. The post emphasizes aligning the choice of search tool with specific business needs and use cases, considering pricing models and feature sets.