Glean is a productivity startup that has developed a smart enterprise search assistant by indexing and understanding the context of documents from dozens of products through the use of 100+ APIs. As information complexity increases, Glean gives knowledge workers a Google like experience to more efficiently search through content and employee capabilities.

Founding Date

Jan 1, 2019

Headquarters

Palo Alto, California

Total Funding

$ 358M

Stage

series d

Employees

251-500

Careers at Glean

Memo

Updated

May 3, 2024

Reading Time

18 min

Thesis

The average enterprise has over 1K applications, only 29% of which actively talk with one another and share data. This trend will likely accelerate, as end-user spending on public cloud services is estimated to reach $679 billion in 2024 and exceed $1 trillion in 2027. With this scale comes information fragmentation among different applications, making searching for and finding relevant information a significant problem.

The average worker in the US uses 11 different applications in their day-to-day work as of May 2023, spending an average of 13 minutes searching for information before asking for help. On average, workers spend ~20% of their workweek trying to understand the documents, information, or people they need to do their jobs, leading to significant inefficiency and potentially even employee turnover. Some estimates indicate this inefficiency could cost Fortune 500 companies over $12 billion per year.

In addition to information fragmentation, the shift to distributed work environments has made it crucial for companies to provide employees with efficient tools for self-serving, onboarding, and finding information. The need to streamline employee onboarding creates an opportunity for software to help new workers feel like they are thriving in their new jobs. Progress in the capability of AI to understand natural language has led to improved search functionality, which could improve employee collaboration and information sharing.

That’s where Glean comes in. Glean is a productivity startup that has developed a smart enterprise search assistant by indexing and understanding the context of documents from dozens of products through the use of 100+ APIs. As information complexity increases, Glean gives knowledge workers a Google-like experience to more efficiently search through content and employee capabilities.

Weekly Newsletter

Subscribe to the Research Rundown

Founding Story

Source: Glean

Glean was founded in 2019 by Arvind Jain (CEO), T.R. Vishwanath (Infrastructure Lead), Tony Gentilcore (Product Engineering Lead), and Piyush Prahladka (former Head of Search & AI).

Prior to founding Glean, Jain had co-founded Rubrik* in 2014, a data security business that reached a $1 billion valuation within just over a year of emerging from stealth. While leading Rubrik, Jain and his team utilized a tech stack made up of 300+ cloud applications. With data scattered across so many pieces of software, Jain found his own productivity stunted by the time spent locating the right information. During a survey at Rubrik, he observed that finding information was the “single biggest employee environment issue”. Jain realized that no product existed in the market that was solving the problem of knowledge fragmentation within enterprises.

Jain had spent more than a decade from 2003 to 2014 working at Google and previously held leadership positions at Akamai and Microsoft. He was inspired to solve the problem by building a Google-like search engine in front of business content and assembled a team of Google and Facebook veterans. Vishwanath had prior experience as a principal software engineer at Facebook from 2010 to 2019, working on areas like News Feed ranking, ads, and Facebook’s developer platform. Gentilcore and Prahladka both spent almost a decade at Google as Senior Staff Software Engineers. Prahladka served as the technical and engineering lead in the Google Maps and Search Quality Ranking teams from 2005 to 2017, Gentilcore had previous experience in modernizing the web search interface and leading Chrome’s Speed Team between 2006 and 2016.

Given their backgrounds, the co-founding team was able to understand how ubiquitous the enterprise search problem was, and what differentiated it from consumer search. Unlike webpages linked together, enterprise search involves challenges such as data governance and linking between custom APIs for SaaS applications. To make the product useful for workers across an enterprise, Glean used large language models to perform semantic matching. This allowed users to ask a question in natural language, receive results from across all the applications the business uses, and tailor responses according to the user’s job function.

In March 2019, Glean raised a $15 million Series A and was launched out of an incubation space in Kleiner Perkins’ Menlo Park office, choosing to build in stealth for several years before officially launching in 2021. The company’s initial target customers were technology companies, and as of March 2024, the company was looking to expand to financial services, retail, and manufacturing.

Product

Source: Glean

Glean is a unified search product that indexes dozens of applications, understanding context, language, behavior, and employee relationships, to find personalized answers to questions. The product is built on company knowledge and content, with a focus on permissioning and data governance in mind. Glean is a layer on top of a company’s software applications that users can engage with through a web app, new tab page, sidebar search, native search, or Slack demand.

To provide its core features, Glean retrains language models on a company’s unique knowledge base to develop a thorough understanding of content, language, people, and relationships. It operates based on the enterprise knowledge graph, a real-time model of all the indexed information within a company.

Workplace Search

Source: Lightspeed

Glean’s technology surfaces personalized results based on the user’s role and relationships. In April 2023, Glean launched several generative AI features to streamline search results, such as AI answers, which generate a single concise answer to a query. Other features like expert detection and in-context recommendation connect users with subject matter experts within the company to provide additional context about their search query.

Glean Assistant

Source: Glean

In June 2023, Glean launched Glean Assistant, a ChatGPT-powered chatbot that can leverage the existing corporate knowledge graph that Glean leverages for search to provide concise responses to user questions. Glean Assistant can provide direct questions to queries, or attempt to provide a response as part of a search on Glean’s broader platform. These results also provide personalized and permissions-aware responses to employees, such as helping with code for engineers, capturing activity outside ticketing systems for customer support teams, and crafting pitches for sales teams.

Knowledge Management

Source: Glean

Glean can curate collections of information, aggregate verified answers, and enable managers to create and share “Go Links” to help employees navigate to common collections of resources. For example, a company can assemble a “product-roadmap” Go Link so that users can easily access all the necessary internal resources to create a new product roadmap.

Work Hub

Source: Glean

Glean has built out a “home page” that serves as the go-to spot for employees to find AI-generated recommendations for documents to read through or people with relevant knowledge. This home page includes company announcements, employee directories, a calendar, and multiple widgets to help supercharge employee collaboration like related people, recent work, and availability to meet.

Connectors

Source: Glean

A critical piece of Glean’s ability to integrate across an organization's various sources of knowledge is its ability to connect with the disparate applications being used. Glean has built over 100 connectors to different systems that customers rely on. Glean’s ability to expand into larger organizations is significantly limited if the product is unable to ingest knowledge information from a particular core system.

These applications are connected via native, push API, and web history-based connectors. In addition to combining content from these sources, it also takes into account the metadata, identity data, permissions data, and activity data to provide secure and personalized responses to different users.

Platform

In addition to Glean’s Search and Chat, the Glean Platform allows enterprises to create their own generative AI applications utilizing the company’s knowledge graph. Users can create conversational applications by limiting the data sources it can reference, and publish it for tasks like answering FAQs or handling HR requests. Developers can also use Glean Tools SDK to let users conduct actions through the Glean Assistant conversation window.

Market

Customer

Source: Glean

Glean began by targeting fast-growing technology companies, based on the founding team’s previous experiences. This initial customer segment of companies with a workforce of 500 to 2K employees was chosen because of their propensity to move fast and be “willing to experiment”, according to Jain.

According to an interview with Outreach, a customer of Glean, Glean adds the most value to organizations that have passed an initial setup phase and are now entering into a growth phase with ~100 employees and more. The product is used by end users like engineers, account executives, support agents, and sales engineers.

As of February 2024, Glean serves 200 enterprise customers, including Duolingo, Grammarly, Webflow, Confluent, and Sony. As of March 2024, Glean was seeking to expand its customer base in industries like financial services, retail, and manufacturing. In February 2024, Citi Ventures confirmed that the bank “will do a pilot evaluation and might end up as a customer”.

Market Size

The global enterprise search market is projected to grow at a CAGR of 9.6% from $5.6 billion in 2023 to $13 billion in 2032. This growth can be attributed to growing data volumes and advanced data complexity within organizations. Along with the data itself, the average number of applications used by a desk worker at an enterprise has also increased. Employees are increasingly spending more time searching for information, with 47% of desk workers struggling to find necessary data for their jobs.

Within the global search market, the multimedia search segment is expected to account for the highest CAGR of 12% from 2023 to 2032, ahead of conversational and multilingual search. This provides an opportunity for Glean to expand from conversational and semantic search solutions as well.

The incorporation of AI-based tools has seen support from organizations and CIOs globally. Generative AI as a percentage of IT budgets is projected to increase from 1.5% in 2023 to 4.3% in 2025. 43% of IT leaders consider customer chat to be the most crucial generative AI capability for making a business impact.

Competition

The challenge of enterprise search has been a well-established problem that a number of companies have tried to solve. In 2007, a company called Powerset raised $12.5 million from investors like Peter Thiel and Luke Nosek, founders of PayPal, and Reid Hoffman, founder of Linkedin. In 2008, the company was acquired by Microsoft for $100 million.

Originally, technology like Powerset was expected to become an enterprise search juggernaut built around Microsoft’s SharePoint collaboration platform. However, Barney Pell, the founder of Powerset, focused primarily on Bing, and much of the excitement around search in the early 2010s was more focused on consumer use cases, rather than enterprise.

As a result, Glean still considers the status quo of employees having to search through various systems to be the most likely alternative that people might choose in lieu of Glean. As Arvind Jain, Glean’s CEO, explained in one interview:

“Glean’s biggest competitor is the status quo: employees continuing to deal with the complexity of finding the information and people they need at work. In a typical sales process, potential customers often require Glean to first start with a pilot to demonstrate how much value implementing Glean can provide.”

Of the solutions that do exist in this space, the majority are aligned to one of the large tech companies, such as Microsoft and Amazon.

Source: Gartner

Established Tech Companies

Microsoft: Microsoft has multiple solutions in the enterprise search space. SharePoint Syntex was launched in 2020 and made available to all Microsoft 365 users in 2022. The product offers content storage with AI integrated into user workflows in order to automatically add tags, and index high volumes of content, so users can search effectively. Unlike Glean, SharePoint doesn’t connect with every information source within a company, limiting its access to Office 365. Within Microsoft’s Azure cloud service, the company provides Azure Cognitive Search, an information retrieval system within a customer’s web applications and data, both for internal enterprise use cases and external website or ecommerce search.

Google Cloud Search: Google announced Google Cloud Search in 2017 as a search platform spread “across G Suite products, including Drive, Gmail, Sites, Calendar, Docs, Contacts and more.” Originally, the product was focused on being seamlessly integrated across Google Workspace apps, but increasingly Google has launched similar connections to external platforms as other enterprise search offerings. The product includes connections to GitHub, Confluence, Jira, and Slack among others.

Amazon: In 2020, Amazon announced the release of Amazon Kendra, an enterprise search platform that enables users to ask contextual questions and search across silos for relevant information, both within Amazon’s ecosystem (e.g., S3) and external (Salesforce, Slack, etc.) Amazon also offers Amazon CloudSearch, a cloud-based search service that is primarily focused on external use cases like a website or ecommerce store, and Amazon OpenSearch Service, derived from Elasticsearch, which is primarily for application performance review, rather than knowledge management.

Elastic: Founded in 2012 by the creators of the popular open-source project Elasticsearch, Elastic provides software products for developers, startups, and enterprises to make massive amounts of complex structured and unstructured data usable. By focusing on scalability, ease-of-use, and ease of integration, Elastic’s products are used for real-time search, logging, analytics, and security to power internal and external applications for organizations like Cisco, eBay, Goldman Sachs, and Groupon. Elastic went public in June 2018 after raising a total of $162 million from investors like Benchmark, Index, and NEA. Elastic mostly works on the back end of enterprises, powering external application interfaces without users ever realizing how their searches are being executed, and has not shown a desire yet to compete with Glean’s vision to become the Google of internal company data search.

Newer Entrants

Coveo: Founded in 2005 in Canada, Coveo is an AI search solution for ecommerce, websites, customer service, and workplaces. The company went public in November 2021, raising $158.3 million (CAD 215 million) for its IPO. Coveo reported a revenue of $112 million in 2023, up by 30% from 2022. As of April 2024, Coveo has 700+ enterprise customers, including Adobe, Formica, Salesforce, and Manulife. Unlike Glean, which solely focuses on internal enterprise search, Coveo’s product suite includes ecommerce and website offerings as well.

Sinequa: Founded in 2002, Sinequa is an enterprise search company, headquartered in France. The company has raised a total of $28.3 million in funding as of April 2024. It raised a $23 million Series B at an undisclosed valuation in July 2019 led by Jolt Capital. The company expanded to the United States in 2014 and mainly serves industries like healthcare, life sciences, manufacturing, financial services, law firms, and government and defense. As of April 2024, its customers include Pfizer, NASA, BASF, Siemens, and TotalEnergies. While both Sinequa and Glean offer AI-powered search and generative AI capabilities to their customers, the biggest differentiator between the two is the target customer segment.

Lucidworks: Lucidworks is an AI-powered search and product discovery company, founded in 2007. The company has raised a total of $209 million in funding as of April 2024. In August 2019, it raised a $100 million Series F at a $370 million valuation, led by Francisco Partners. Lucidworks offers solutions for both a company’s internal use as well as external-facing roles like customer service and ecommerce search. As of 2024, its customers include Crate & Barrel, Morgan Stanley, Northwell Health, Cisco, and REI. Compared to Glean’s focus on technology companies, Lucidworks serves retail, healthcare, manufacturing, B2B commerce, and public sector industries.

Vectara: Founded in 2022 in Palo Alto, Vectara is a generative conversational search platform that seeks to provide a ChatGPT-like experience for business users looking to engage with their internal data. The company raised a $28.5 million seed round in June 2023, led by Race Capital. In February 2024, Vectara launched a new module Vectara Chat, that allows users to create their own chatbots using domain-specific data. The company positions itself as a “developer-friendly, API-first search platform”.

Neeva: Neeva, founded in 2019, was launched as a rival to Google’s consumer search business. The company raised a $40 million Series B in March 2021, bringing its total funding amount to $77.5 million. By 2022, Neeva had grown to one million monthly users and was expanding to Canada. After facing challenges in creating a sustainable user acquisition funnel, it announced a pivot to AI-powered enterprise search in May 2023. Just three days after the announced pivot, Snowflake announced that it had acquired Neeva for an undisclosed amount.

Traditionally, enterprise search companies are focused on enabling users to search for information across their employer’s internal knowledge databases. Some products, like Azure Cognitive Search or Amazon CloudSearch, are focused on enabling search functionality within an existing cloud ecosystem. With the acquisition, Neeva is expected to provide a higher-quality search capability within Snowflake’s cloud ecosystem.

Business Model

Glean earns its revenue through a per-user monthly fee, based on annual subscription contracts. The company has not made its pricing model public. As of February 2024, Jain said the custom pricing is dependent on the number of employees using the product each month. One interview indicated that early customers of Glean like Outreach paid a flat rate of ~$50K per year, regardless of the number of employees using the software.

Traction

Glean's annualized revenue reached $39 million in January 2024, a ~4x increase from the $10 million recorded in 2023. As of February 2024, Glean served 200 enterprise customers, including Duolingo, Amplitude, Databricks, Plaid, and Vanta. As of March 2024, the company had 337 employees.

In February 2024, Jain announced plans to use $200 million in Series D funding to double the number of employees to 700 by the end of the year. Other plans for the capital infusion included improving the product and “building out a robust go-to-market motion.”

Glean announced a partnership with NVIDIA in March 2024, enabling its customers to use NVIDIA’s NIM microservices for Glean Assistant. Along with Glean’s retrieval system, this will allow users to choose from a variety of large language models and build their own AI-based assistants.

Valuation

In February 2024, Glean announced a $200 million Series D at a $2.2 billion valuation. The company has raised a total of $358.2 million in funding as of May 2024. The Series D was co-led by existing investors Kleiner Perkins and Lightspeed Venture Partners and also included General Catalyst, Sequoia Capital, ICONIQ, and Citigroup. Its valuation at its Series D represented a 2.2x increase from the $1 billion valuation in May 2022 when Glean raised a $100 million Series C led by Sequoia.

Key Opportunities

AI Assistant for the Enterprise

With rapid advancement in LLMs like GPT-4, the practicality of an enterprise AI assistant becomes more probable. The vision for this kind of product would be an AI assistant that takes goals or tasks and leverages AI to find answers, or even progress towards completing tasks. In one estimate, the market for business process automation technologies — technologies that streamline enterprise customer-facing and back-office workloads — will grow from $9.8 billion in 2020 to $19.6 billion by 2026. In addition to analyzing, extracting, and synthesizing information, Glean can add task completion features to the Glean Chat AI assistant.

Employee Data Graph

Glean is uniquely positioned because it has access to the entire corpus of a company’s internal data. Having built out the employee knowledge graph, Glean now knows how every employee is connected to each other. Similar to how Rippling has built out a suite of tools from its employee knowledge graph, Glean can leverage its platform position and source of truth within an enterprise to potentially build out knowledge products in specific use cases like HR, payroll, app management, device management, and other tools, expanding its potential market size.

Global Expansion

The Asia-Pacific region is anticipated to be the fastest-growing market for enterprise search solutions, with a projected CAGR of 13.8% from 2023 to 2032. The region is also the fastest-growing financial services application market, projected to reach $271.7 billion by 2029. Expansion into this geography can serve Glean well, especially as it ventures into industries like financial services.

Key Risks

Unproven Market

Enterprise search is a difficult market. Consumer search has been able to succeed largely because of the massive amount of data on the public internet these types of products have to work with. Other companies, like Elastic and Algolia, have found some success enabling search capabilities for developers.

Regulation

Increasing privacy concerns and frequently changing policies around data and compliance pose obstacles for Glean. Since the platform connects the proprietary data of an organization, including internal chat data, data security risks are a challenge, especially with Glean seeking to expand into more regulated industries like financial services. Additionally, problems like hallucinations – where an underlying LLM generates grammatically correct but factually inaccurate data – are a serious concern, and products like Glean need to continue enforcing mitigation techniques to ensure reliable responses.

Adoption and Retention

Enterprises cannot draw any inferences for users or items if it hasn’t gathered enough information, and this can limit the size of customers Glean can target. In one interview, a Glean customer indicated that even post-launch for Glean, adoption among employees could be as low as 20-40%. Adoption can also be relatively uneven across different teams. Glean’s initial target buyers were companies with 500 to 2K employees, which are in their experimental, fast-moving stage. With fewer employees and thus a lesser need for knowledge management capabilities, Glean could find it challenging to market to SMEs.

Weekly Newsletter

Subscribe to the Research Rundown

Summary

As an enterprise grows, so does the volume and complexity of its information. Being able to structure existing content across diverse repositories and surface relevant information quickly yields benefits like increased productivity and enhanced collaboration. Glean is attempting to reform the way knowledge workers find and consume information. By integrating with as many knowledge databases within an enterprise, Glean can build a contextual knowledge graph to understand what information is critical, where it’s stored, and who at the company has any possible additional context on that information. As Glean continues to expand into larger enterprises, the company will have to demonstrate the ability to handle an increasingly complex knowledge graph to continue to serve customers effectively.

*Contrary is an investor in Rubrik through one or more affiliates.

Disclosure: Nothing presented within this article is intended to constitute legal, business, investment or tax advice, and under no circumstances should any information provided herein be used or considered as an offer to sell or a solicitation of an offer to buy an interest in any investment fund managed by Contrary LLC (“Contrary”) nor does such information constitute an offer to provide investment advisory services. Information provided reflects Contrary’s views as of a time, whereby such views are subject to change at any point and Contrary shall not be obligated to provide notice of any change. Companies mentioned in this article may be a representative sample of portfolio companies in which Contrary has invested in which the author believes such companies fit the objective criteria stated in commentary, which do not reflect all investments made by Contrary. No assumptions should be made that investments listed above were or will be profitable. Due to various risks and uncertainties, actual events, results or the actual experience may differ materially from those reflected or contemplated in these statements. Nothing contained in this article may be relied upon as a guarantee or assurance as to the future success of any particular company. Past performance is not indicative of future results. A list of investments made by Contrary (excluding investments for which the issuer has not provided permission for Contrary to disclose publicly, Fund of Fund investments and investments in which total invested capital is no more than $50,000) is available at www.contrary.com/investments.

Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by Contrary. While taken from sources believed to be reliable, Contrary has not independently verified such information and makes no representations about the enduring accuracy of the information or its appropriateness for a given situation. Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. Please see www.contrary.com/legal for additional important information.

Authors

Michael Chen

Fellow

See articles

Shmi Bhan

Fellow

See articles

© 2024 Contrary Research · All rights reserved

Privacy Policy