Glean: How to Outpace Competitors in Enterprise AI (and Frustrate OpenAI in the Process)

The question of whether AI, especially generative AI, increases productivity has been coming up more often, particularly with the billions invested in AI technologies. Meanwhile, Glean offers a tool that directly addresses productivity concerns in enterprises. Not driven by AI hype or ChatGPT, but by inefficiencies in large companies with over 1,000 employees, Glean’s founders – engineers through and through – have been using AI since 2019 to solve the problem of too many information silos and actually enhance productivity.

Not only did they raise a massive $260 million funding round in September 2024, reaching a $4.6 billion valuation – doubling what it was worth just six months ago – but they also made it onto several notable lists this year: IA40 by Madrona, Cloud 100 by Forbes, and “Don’t Invest in These 5 Guys” by OpenAI.

Why the distinction of being personally ousted by the biggest unicorn in GenAI (OpenAI, in this case)? Let’s explore in this new episode of the GenAI Unicorn Series.

In today’s episode:

You know what they say about secret agents? Once an agent – always an agent. Apparently, search engineers are of the same sort.

Arvind Jain – CEO and co-founder of Glean – worked at Google for a long time. His self-identification is as a “search engineer.” Even after he left Google and built Rubrik – a cloud data management company that focuses on providing backup, recovery, and cybersecurity solutions for enterprises – if there was a problem with searching for stuff, his professional ears would prickle.

And there were. Rubrik was growing rapidly, crossing 1,000 employees, but productivity had first slowed and then stagnated. An engineer who was writing 300 lines of code a day before was now writing 50 lines of code. It was really low. Same for salespeople. Arvind set an internal survey to find out what the problem was, how they could do better, and what things were coming in the way of employees doing good work.

The largest complaint was: “Hey, I can’t find anything in this company. I don’t know where to go and look for information, but I need that. And I also don’t know who to go and ask for help when I need help,” and so this was a big problem.

“So when people are saying, we can’t find things, the first thing that comes to my mind is, “Hey, we should actually have a search product,” thought Jain.

Reminiscing on his years at Google, Jain realized that the problem with internal search wasn't just a Rubrik issue – it was universal. Even at Google – one of the most powerful search engines – there was a huge problem locating the right information internally. So, he talked to a few more companies to make sure he wasn't hallucinating the problem, and then involved the best people he knew from the research engineering army.

Tony Gentilcore and Piyush Prahladka were Google veterans, while T.R. Vishwanath had worked at Microsoft and Facebook.

They became Arvind Jain’s co-founders at Glean, which they started building in 2019. 

Do you know how long they were in stealth mode? Two and a half years!

In 2019, thanks to Rubrik’s success, Arvind Jain had gained the trust of investors. As a result, Glean quickly raised $15 million in Series A funding, and the hard work began. 

One might assume that top-notch search engineers could build a search engine overnight, but it took Glean two and a half years of intense development in stealth mode before they could present the first iteration of their assistive search tool to the world.

Building a search engine is inherently challenging due to the need for real-time indexing of diverse data formats and developing algorithms capable of delivering precise, relevant results. This complexity increases when integrating semantic understanding, where the engine must not only match keywords but also interpret the meaning behind a query to provide contextually accurate results. Additionally, it must scale to handle vast amounts of information while maintaining speed and accuracy.

For enterprise environments, the challenge is even greater due to the need for personalization. Each search must account for user-specific access permissions, ensuring employees only access data they are authorized to see. This requires incorporating sophisticated security layers, adding complexity beyond that faced by general-purpose search engines. Real-time updates, data governance, and maintaining relevance across multiple systems further complicate enterprise search engine development.

Glean was launched in 2021 as a “work assistant with intuition,” the first of many names that the marketing team would experiment with as they fine-tuned the brand's messaging to capture the true essence of its capabilities and appeal to a broader audience.

In 2019 there was no AI hype, so the founders were just concentrating on what they knew was working the best. 

From the outset, the founders integrated language models into the product, specifically from Google’s BERT family, to boost its semantic search capabilities. This wasn’t driven by any particular AI-driven vision at the time, but rather by their deep understanding of how Google had enhanced its own search. BERT models, made publicly available in 2018-2019, fit seamlessly into Glean’s technology stack. While these early models didn’t generate text, they could interpret meaning, enabling more sophisticated search functionality.

By 2021, when Glean emerged from stealth mode, the company introduced an assistive search tool aimed at enterprises. It offered a unified search experience, retrieving information from multiple applications like Salesforce and Slack, centralizing data in a single interface. The system’s semantic understanding allowed it to recognize equivalent phrases such as “quarterly goals” and “Q1 areas of focus,” making search more intuitive. Tailored search results enhanced user experience, adjusting based on roles, so a salesperson and an engineer would receive results personalized to their needs. Deep learning technology played a role in improving both relevance and personalization. Glean also integrated seamlessly with a variety of enterprise applications, supporting the growing trend towards cloud-based and SaaS solutions.

By this point, with $55 million in venture funding, Glean was positioned as an innovative answer to the widespread problem of fragmented information across enterprise systems.

Yes, employees in many companies often complain about not finding the right information. But when it comes to productivity, they struggle to pinpoint what’s really affecting it and how to measure those issues. When a potential solution is introduced, they initially resist it, questioning whether it’s necessary at all.

Arvind faced this exact challenge in creating a new product category. People were accustomed to getting by without it, often saying, “Things are fine as they are,” making the sale even harder. The real task was showing them that, while they had complaints, the solution they initially opposed could actually address deeper productivity issues they hadn’t yet defined.

Arvind Jain didn’t even want to be an entrepreneur in the first place. He wasn't initially planning to leave Google to start Rubrik. He was content with his work at Google, where he had made significant contributions to projects like Google Search, YouTube, and Google Maps. When his future co-founder Bipul Sinha approached him with the idea for Rubrik in 2013, Jain was initially hesitant, unsure why Sinha chose him and unsure about becoming an entrepreneur.

Sinha convinced him by making the point that Jain would build the product while Sinha handled the business side.

With Glean, it was different. Since it was Arvind’s idea, he had to step outside his comfort zone once again and turn from a builder into more of a hustler. The first year at Glean was mostly spent on LinkedIn recruiting the right team.

By the second year, his role shifted again. With the team in place and the product nearing readiness, Arvind found himself acting as the company’s SDR (sales development representative). Now, his focus was on selling the vision, reaching out to potential clients, and ensuring that people understood the value Glean could bring. The second year was all about laying the groundwork for bringing their product to market, making sure the right conversations were happening with the right people. Building the team was hard, but shifting into sales mode was a whole new challenge.

Arvind Jain focused on evangelizing Glean’s product by targeting forward-thinking CIOs eager to drive change within their organizations. The idea was to position them as innovators, almost heroes, who were transforming their companies. The challenge, however, was that there was often no dedicated budget for such tools. Glean’s pitch emphasized its ability to solve the ongoing challenges of knowledge discovery and enhance productivity. Early feedback was positive, with CIOs reporting that Glean had relieved major pain points and earned praise from employees. 

It was all good on paper and during meetings, but the effect was not sufficient. Marketing efforts were under-invested, causing the company to lag and slowing Glean’s growth. 

In 2020, the pandemic came as a savior, shifting the work landscape in Glean’s favor. As remote work surged, the need for seamless access to company knowledge became critical. Glean immediately adapted its product to meet these needs, evolving from a simple search tool to a comprehensive knowledge hub with features like browser integration.

Glean’s value resonated with companies adjusting to the remote environment, quickly becoming an essential tool for organizations maintaining productivity despite dispersed teams. By addressing real-world problems highlighted by the pandemic, Glean solidified its place in the modern workplace. That alone was working fine, and the growth was stable.

The real burst, though, came with ChatGPT.

The launch of ChatGPT was a turning point for Glean, aligning perfectly with its expertise in language models and existing work, significantly boosting business growth. This event also shifted the market from building demand to widespread enterprise adoption of AI, propelling Glean to new heights in success, client integration, and valuations.

A key driver behind this growth was the evolution of LLMs, which advanced to not only understand but also generate coherent answers. This transformation positioned Glean to go beyond simple document retrieval, allowing AI to read, interpret, and generate responses. Glean quickly introduced a few important updates, becoming an all-in-one enterprise knowledge and communication base, and posing a threat to OpenAI for a significant share of the enterprise market.

“We always called our product as a search engine for work. But that’s not interesting to people. So we’re now going to say, “We’re a RAG engine.” So you have to definitely look for what the market trends are and if you can capitalize on those, that is what gives you that extreme momentum.” Arvind Jay

Retrieval Augmented Generation (RAG) is indeed a core technology for Glean, playing a crucial role in their enterprise AI solution.

“We were the first company to actually build vector search and embeddings and these other terms you hear these days in the AI world,” said Arvind.

Arvind Jain highlights that hallucinations are a common issue in all AI models, where they generate incorrect or nonsensical information. To reduce hallucinations, Glean grounds AI models to specific sets of internal company data, ensuring the AI works only with relevant, verified documents. Additionally, Glean implements a fact-checking process where AI-generated content is cross-referenced with company knowledge to ensure accuracy. While these techniques significantly improve reliability, occasional mistakes still occur. Jain believes focusing AI on well-curated corporate data is key to minimizing hallucinations in business settings​.

Glean has seen a 4x revenue growth from $10 million in 2023 to $39 million in 2024 and expanded its workforce significantly, growing from 337 employees in March 2024 to 614 employees by September 2024.

Initially started in San Francisco, California, in July 2024, Glean opened an office in India.

The Glean team strategically targeted mid-sized companies (like Rubrik was) with a workforce of 500 to 2,000 employees. These companies 1) face significant knowledge discovery challenges, 2) have the budgets for solutions like Glean, and 3) are agile enough for quick adoption.

Glean’s revenue model involves charging a monthly per-seat subscription based on annual contracts, keeping pricing simple regardless of how many systems are connected. The company has not made its pricing model public.

As of September 2024, Glean serves over 200 enterprise customers, including Duolingo, Grammarly, and Sony, with a rapidly growing user base.

There is a bunch of lists that offer competitors for Glean. Our analysis shows that direct competitors are companies focused on enterprise search, knowledge management, and AI-powered solutions like Microsoft (SharePoint Syntex, Azure Cognitive Search), Google Cloud Search, Amazon Kendra, Elastic Search, Lucidworks, Coveo, Sinequa, Moveworks, Vectara (interview with Amr Awadallah) , Hebbia, and Neeva. The other companies are either adjacent competitors in related fields or serve more specialized or niche markets.

Here we come to OpenAI request not to invest into Glean. OpenAI has ambitious revenue growth projections, aiming to reach $11.6 billion in 2025 from $3.7 billion in 2024. To achieve this substantial growth, the company likely intends to sell more of its tools to enterprises and end users. Glean's presence in the enterprise search space could potentially interfere with these plans.

So far, out of the five companies (SSI, Anthropic, XAI, Perplexity, Glean), Glean has the most established connections with enterprises and already utilizes OpenAI technologies for its products and offerings. By 2025, the competitive landscape expanded further — with autonomous coding agents entering the enterprise space. Cognition AI's Devin redefined what AI agents can do for software teams, adding a new dimension to the race for enterprise AI dominance.

I’ve read and listened to so many interviews about Arvind Jain that it’s clear he was the driving force that kept Glean on track. Despite inconsistent marketing – one moment Glean is an AI assistant, then Google for Work and ChatGPT for Work, now it’s a RAG engine with the tongue-twister tagline “Put AI to work. At work” – Jain’s leadership and relentless hustle made the real difference. He steered the company through the remote work boom and capitalized on the ChatGPT wave.

While others, including OpenAI, are talking about AI enhancing productivity, Glean is actually delivering it at the enterprise level, addressing real pain points in knowledge discovery. Now with GenAI under it’s hood it ships really fasJain’s ability to keep Glean moving forward, even with initial resistance and marketing struggles, proves that sometimes a CEO’s hustle and readiness to listen are more valuable than flashy campaigns.