Glean has launched Glean Chat, a generative AI-based assistant for boosting enterprise productivity

Glean, a company founded in 2019 by former Google, Microsoft, and Meta employees, has released a new generative AI-based assistant called Glean Chat, designed to boost productivity and efficiency in enterprises. Its purpose is to help employees find information quickly across an enterprise’s applications and content repositories with source citations via a conversational search interface. In this article, we’ll explore Glean Chat’s features, functionality, Glean’s infrastructure and funding, as well as the competition.

Features of Glean Chat

Glean Chat is touted as the “Power BI of unstructured data,” with its main feature being its ability to help employees find information easily and efficiently. It offers an experience very similar to OpenAI’s ChatGPT but is limited to an enterprise’s content and resource boundaries. What makes Glean Chat stand out is its ability to provide source citations, which makes it easier for employees to find the needed information. It is designed to be user-friendly, which means employees don’t have to be tech-savvy to use it.

Functionality of Glean Chat

When a user makes a natural language-based query, the company’s search technology uses APIs to check all the content and activity – including information in applications – pertaining to the query before storing it in a customer’s cloud environment. This process ensures that the query is directed to the appropriate person or team.

Layers of Glean

Glean is built on five layers consisting of infrastructure, connectors, a governance engine, the company’s knowledge graph, and an adaptive AI layer. The infrastructure layer consists of the basic hardware and software required to run Glean, while the connectors layer links Glean to the various enterprise content repositories and applications. The governance engine ensures that all the information stored on Glean is compliant with regulations and company policies. The knowledge graph captures the enterprise’s information in an organized format, allowing Glean Chat to access it more easily. Lastly, the adaptive AI layer is responsible for Glean’s generative AI capabilities.

The adaptive AI layer of Glean

The adaptive AI layer uses information from the knowledge graph and runs it through LLM embeddings for semantic understanding, as well as large language models for generative AI. It is important to note that Glean utilizes a mix of large language models, including OpenAI’s GPT-4 and transformer models from Google such as BERT. The adaptive AI layer is responsible for analyzing queries and providing relevant responses.

Competitors in the industry

Glean Chat faces an uphill task when it comes to carving out a space in the crowded generative AI market, as there are many competitors with similar offerings. Some of the competitors include OpenAI, IBM Watson, Google, and Amazon AWS. OpenAI’s GPT-3 and GPT-4 are among the most advanced language models in the market, and their capabilities are extensive.

Please provide more context to your sentence for me to understand what you’re requesting

Glean Chat will be priced on a per-seat basis and offered as a premium add-on to Glean’s core search product. The company offers enterprise-level pricing for Glean, which means that the price can vary depending on the customer’s needs.

Funds and Customers

Glean has raised about $155 million to date from investors such as Sequoia, Lightspeed, Slack Fund, General Catalyst, and Kleiner Perkins. The company claims that it already has over 100 enterprise customers, including Databricks, Vanta, Plaid, Grammarly, Okta, Samsara, Niantic, Greenhouse, Duolingo, Wealthsimple, and Confluent. With such top enterprise customers, it shows that the product has a promising future.

In conclusion, Glean Chat is an exciting and innovative product designed to provide an efficient and user-friendly conversational search interface. Its generative AI capabilities are impressive, and it stands out from competitors with its source citation feature. The pricing structure is reasonable and flexible, making it accessible to enterprises of all sizes. Glean has a bright future, and it will be interesting to see how it performs in the highly competitive generative AI market.

Explore more

AI and Generative AI Transform Global Corporate Banking

The high-stakes world of global corporate finance has finally severed its ties to the sluggish, paper-heavy traditions of the past, replacing the clatter of manual data entry with the silent, lightning-fast processing of neural networks. While the industry once viewed artificial intelligence as a speculative luxury confined to the periphery of experimental “innovation labs,” it has now matured into the

Is Auditability the New Standard for Agentic AI in Finance?

The days when a financial analyst could be mesmerized by a chatbot simply generating a coherent market summary have vanished, replaced by a rigorous demand for structural transparency. As financial institutions pivot from experimental generative models to autonomous agents capable of managing liquidity and executing trades, the “wow factor” has been eclipsed by the cold reality of production-grade requirements. In

How to Bridge the Execution Gap in Customer Experience

The modern enterprise often functions like a sophisticated supercomputer that possesses every piece of relevant information about a customer yet remains fundamentally incapable of addressing a simple inquiry without requiring the individual to repeat their identity multiple times across different departments. This jarring reality highlights a systemic failure known as the execution gap—a void where multi-million dollar investments in marketing

Trend Analysis: AI Driven DevSecOps Orchestration

The velocity of software production has reached a point where human intervention is no longer the primary driver of development, but rather the most significant bottleneck in the security lifecycle. As generative tools produce massive volumes of functional code in seconds, the traditional manual review process has effectively crumbled under the weight of machine-generated output. This shift has created a

Navigating Kubernetes Complexity With FinOps and DevOps Culture

The rapid transition from static virtual machine environments to the fluid, containerized architecture of Kubernetes has effectively rewritten the rules of modern infrastructure management. While this shift has empowered engineering teams to deploy at an unprecedented velocity, it has simultaneously introduced a layer of financial complexity that traditional billing models are ill-equipped to handle. As organizations navigate the current landscape,