Built with BigQuery and Google AI: How Glean Enhances Enterprise Search Quality and Relevance for Teams
公司规模
Large Corporate
国家
- United States
产品
- Glean
- Google Cloud Dataflow
- Vertex AI
- BigQuery
- Looker Studio
技术栈
- Google Cloud
- Kubernetes
- Tensor Processing Units (TPUs)
- Cloud SQL
- Cloud Storage
实施规模
- Enterprise-wide Deployment
影响指标
- Customer Satisfaction
- Digital Expertise
- Productivity Improvements
技术
- 分析与建模 - 机器学习
- 平台即服务 (PaaS) - 数据管理平台
- 分析与建模 - 预测分析
适用行业
- Software
- Professional Service
适用功能
- 商业运营
- 产品研发
用例
- 边缘计算与边缘智能
- 远程协作
服务
- 云规划/设计/实施服务
- 数据科学服务
- 系统集成
关于客户
Glean is a company that specializes in providing enterprise search solutions that enhance productivity and reduce frustration for users by delivering personalized and relevant search results. The company focuses on searching across all workplace applications, websites, and data sources used within an enterprise. Glean's search results respect existing permissions, ensuring users only see what they are authorized to view. The company leverages advanced technology to understand user context, language, behavior, and relationships, providing a highly personalized search experience. Glean is built on Google Cloud, utilizing components such as BigQuery, DataFlow, and Vertex AI to deliver its services. The company is part of Google's Built with BigQuery initiative, which supports tech companies in building applications using data and machine learning products.
挑战
Glean faced the challenge of providing a powerful and personalized enterprise search experience across various workplace applications and data sources. The goal was to deliver highly relevant and personalized search results that respect existing permissions and take into account the user's role, projects, collaborators, and company-specific language. This required a robust technology stack capable of processing and analyzing large volumes of data efficiently, while also ensuring high security and scalability. Additionally, Glean needed to measure and optimize user satisfaction with the search results, which involved understanding user actions and identifying when search results were helpful or not.
解决方案
Glean's solution involves leveraging Google Cloud's advanced data processing, analytics, and AI/ML capabilities to build a powerful enterprise search platform. The company uses Google Cloud Dataflow to process and enrich data pipelines, extracting relevant information from various sources and augmenting it with relevance signals before storing it in a search index hosted on Google Kubernetes Engine. Glean also uses Dataflow to generate training data for its models, which are trained on Google Cloud. For analytical workloads, Glean uses BigQuery to store anonymized user actions and compute user satisfaction metrics, which are visualized using Looker Studio. Additionally, Glean uses Vertex AI to train state-of-the-art language models adapted to enterprise-specific language, utilizing TPUs for efficient training and inference. The search is powered by vector search served with encoders and ANN indices trained through Vertex AI. Glean's platform is built on a variety of Google Cloud components, including Cloud GKE, Cloud SQL, Cloud Storage, and more, providing a reliable, secure, scalable, and cost-effective infrastructure.
运营影响
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