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Arvind Jain: Why Now Is the Time to Solve Enterprise Search
2025/2/20
Generative Now | AI Builders on Creating the Future
A
Arvind Jain
Arvind Jain: 我在2019年创立Glean的初衷是解决企业内部信息搜索效率低下的问题。当时，我们并没有预料到AI会像现在这样迅速发展，但我们亲眼目睹了企业内部知识碎片化带来的巨大生产力损失。在Rubrik工作期间，随着公司规模的扩大，我们发现员工在寻找信息和寻找能够提供帮助的同事方面花费了大量时间，这严重影响了工作效率。因此，我决定利用我在Google积累的搜索引擎技术经验，开发一款能够整合企业内部各种数据源的搜索产品，从而提高员工的工作效率。
我们意识到，企业内部的搜索与互联网搜索有很大不同。互联网上的信息是公开且可访问的，而企业内部的信息通常是私密的且受权限控制的。因此，我们必须构建一个安全的、权限感知的搜索系统，以保护企业数据的安全。
Glean的成功之处在于，它能够用自然语言回答问题，解决了传统关键词搜索的局限性。我们率先将Transformer技术应用于企业搜索，并利用Transformer模型进行更高级别的语义搜索，这使得Glean能够更好地理解用户的意图，并返回更准确、更相关的搜索结果。
起初，市场对企业搜索产品的需求并不强烈，许多企业将搜索视为锦上添花而非刚需。因此，我们花了很长时间来教育市场，向企业展示Glean的价值，并证明它能够显著提高员工的工作效率。
ChatGPT的出现间接推动了Glean的发展，因为它让企业看到了类似于Glean这种内部AI助理的巨大潜力。
Glean的架构采用检索-推理的双阶段模式。首先，我们利用自身技术从企业内部检索相关信息；然后，我们利用大型语言模型（如GPT、Cloud等）进行推理和答案生成。我们的架构支持多种大型语言模型，并能够根据任务选择最合适的模型。
我们提供三种核心功能：类似谷歌的搜索、类似ChatGPT的AI助手和一个AI驱动的流程自动化平台，赋能企业员工。
我们不与大型语言模型公司竞争，而是将它们视为合作伙伴，并充分利用它们的创新成果。我们专注于构建一个横向的AI平台，整合企业内部各种数据和AI模型，帮助企业客户整合其AI工作。
为了推动公司内部AI的应用，我要求每位高管都提出一个AI应用案例，并付诸实践。
Glean与Rubrik的不同之处在于，Glean需要创造一个新的市场，并需要在产品发布前达到更高的质量标准。
Michael Mignano: 略
supporting_evidences
Arvind Jain: '...In fact, Glean became the first company, I believe, that has brought the transformer technology to the enterprise in that sense.'
Arvind Jain: '...Somehow, like, you know, like the industry felt that search was a vitamin and not a painkiller...'
Arvind Jain: '...When the world saw ChatGPT and the power that it has, as an enterprise leader, you were thinking about, well, Well, what if, you know, I had something like this inside my company...'
Arvind Jain: '...Now, our architecture is, so we do build models and they're still built on like BERT or modern BERT...'
Arvind Jain: '...The third thing that you can do with Glean, like, so we have a Google-like search, we have a chat GPT-like AI assistant, but then we also have an agent building platform...'
Arvind Jain: '...But what we are doing is, you know, we're giving, you know, our customers a horizontal AI platform...'
Arvind Jain: '...So, so one of the things that we've done, like, you know, I was, I was frustrated personally about it...'
Arvind Jain: '...Versus like in Glean, we came, we built a product and we knew it from day one that we're building a product for which there are no budgets...'
Deep Dive
Arvind Jain, founder of Glean, identified a significant drop in productivity within Rubrik as it scaled. This was attributed to fragmented knowledge across various systems. Glean aimed to solve this by creating a powerful enterprise search platform leveraging transformer-based models for semantic search.
- Productivity drop in Rubrik due to fragmented knowledge
- Glean's vision: powerful enterprise search
- Early adoption of transformer-based models
Shownotes Transcript