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Enterprise Search Engine

4 posts · Nov 2025 – Sep 2026

What this trail covers

Glean's enterprise search engine continues to evolve, focusing on advanced information retrieval techniques. This includes hybrid search models combining lexical and semantic approaches, robust indexing of diverse data sources, and sophisticated ranking algorithms. The system aims to provide permission-aware, cited results across a wide range of enterprise applications, enhancing user productivity and data discoverability. Emerging trends like semantic search, machine learning integration, and AI-powered classification models like Jev are being integrated to improve query understanding, model routing, and result reranking, offering significant improvements in accuracy, latency, and cost efficiency compared to general-purpose LLMs for bounded decision tasks.

All Glean trails
  1. Jev and the return of the zero-shot classifierThis post introduces Jev, a "System One" model designed for typed decisions (classification, scoring, yes-or-no answers) that offers zero-shot flexibility via a hosted API. It details Jev's advantages over general-purpose LLMs, open zero-shot classifiers, and fine-tuned classifiers by comparing tradeoffs in latency, cost, flexibility, and quality. Experiments at Glean demonstrate Jev's effectiveness in query classification (achieving 66.8% agreement against LLM baselines, with potential for significant speedup) and model routing (outperforming a traditional LLM-based router in both accuracy and latency for a simplified 3-expert scenario). It also shows promise in reranking search results, though specialized models still outperform it on certain classification tasks. The post highlights the importance of Jev for tasks where the taxonomy changes frequently, avoiding the need for retraining.
  2. A comprehensive guide to information retrievalThis post provides a foundational overview of information retrieval (IR) principles, models (Boolean, Vector Space, Probabilistic, Language), and core components (document collection, indexing, query processor, ranking algorithm, UI). It highlights Glean's hybrid search approach, combining lexical and semantic methods, and its application in enterprise search. The post also discusses IR quality metrics (precision, recall, F-measure) and emerging trends like semantic search and ML-driven approaches. While not detailing a specific new system build or migration, it elaborates on the core technology powering Glean's enterprise search capabilities.
  3. What is semantic search, and how does it enhance enterprise productivity?This post details the technical underpinnings of semantic search, contrasting it with lexical search. It explains how semantic search understands user intent, performs conceptual mapping, and leverages contextual relevance and cross-platform integration. The post also highlights the continuous learning aspect through machine learning algorithms and provides examples of its application in workplace scenarios, specifically mentioning Glean as an enterprise search platform that incorporates these capabilities.
  4. The definitive guide to AI‑based enterprise search for 2025This post describes the evolution of AI-based enterprise search, highlighting the shift from keyword matching to intent and context understanding. It details the key components of such systems, including the Enterprise Graph for data contextualization, integration with SaaS applications, the role of NLP and LLMs, and crucial security, governance, and compliance features. It specifically calls out code intelligence as a specialized application of AI search for technical teams. While it mentions Glean's Enterprise Graph and its integration capabilities, it does not introduce a new technical capability or a significant architectural change to an existing one. Instead, it provides a high-level overview and strategic considerations for AI-based enterprise search, positioning Glean within this landscape.