- Glean, developed by Arvind Jain of Rubrik, is an AI-powered platform aimed at simplifying data access for employees within enterprises.
- Initially focused on cognitive search akin to Microsoft’s SharePoint Syntex and Amazon Kendra, Glean evolved to connect and analyze data across various databases.
- Glean addresses the common challenge of employees struggling to find necessary information, with 47% of desk workers facing data accessibility issues.
- Despite concerns about privacy and data security, Glean ensures adherence to permissions and offers safeguards against misinterpretations through personalized AI models.
- With a subscription-based revenue model, Glean has seen substantial growth, quadrupling its annual recurring revenue over the past year.
- Glean’s recent $200 million Series D funding round indicates investor confidence and highlights the increasing demand for GenAI solutions in the enterprise sector.
Main AI News:
In the realm of GenAI, prowess is paramount. Despite its shortcomings, GenAI excels at extracting insights from expansive data reservoirs.
Meet Glean, a revolutionary software solution primed to interface with enterprise databases, both internal and external, to field inquiries in plain English from employees, akin to a bespoke ChatGPT. Conceptualized by Arvind Jain, co-founder of cloud data juggernaut Rubrik, Glean emerged from Jain’s astute observations of Rubrik’s staff grappling with information retrieval, a challenge mirrored across various organizations.
“I noticed engineers diverted excessive time from coding, account managers struggled to access pertinent research or presentations crucial for deals, new hires faced protracted onboarding processes, and the list goes on,” Jain disclosed to TechCrunch in an exclusive interview. “This productivity drain posed a significant problem, draining morale and eroding the employee experience.“
It appears Jain’s intuition was on point.
A recent survey by Gartner unveiled that 47% of desk-bound workers encounter hurdles in accessing requisite data for job execution. Concurrently, the proliferation of workplace applications, now averaging 11 per worker compared to six five years prior, compounds this challenge.
In 2019, Jain, alongside a select founding cohort, birthed Glean, initially conceived as an AI-powered search utility tailored for corporate clienteles.
Initial iterations paralleled Microsoft’s SharePoint Syntex and Amazon Kendra, venturing into the domain of “cognitive search.” Employing natural language processing, early Glean showcased an ability to decipher document intricacies and cater to organization-wide search queries.
With time, Glean metamorphosed into a dynamic platform seamlessly interfacing with and scrutinizing a company’s data repositories to furnish employees with pertinent insights—a trajectory echoing the explosive GenAI trend. Presently, Glean assimilates information from diverse sources, including support tickets, chat transcripts, and CRM entries, harnessing GenAI to distill this plethora of data into actionable insights.
One might speculate companies would exercise caution in entrusting proprietary data—especially internal communications—to a GenAI platform capable of such profound scraping and analysis. Such skepticism isn’t unfounded.
A recent poll by Cisco revealed that over a quarter of organizations have barred GenAI usage due to apprehensions surrounding privacy and data security risks. Companies voiced concerns that GenAI tools might jeopardize intellectual property or inadvertently divulge sensitive information, potentially to competitors.
However, Jain assures that Glean prioritizes “security” and “privacy”—albeit within the confines of a cloud-based GenAI platform.
“Glean adheres to the permissions framework stipulated within a company’s data sources (Slack, Teams, Jira, ServiceNow, etc.), ensuring employees only access data within their purview,” Jain asserted. “Furthermore, document deletions within the source application are mirrored within Glean’s system.”
Yet, what about the bane plaguing most GenAI systems—misinterpretations? Is Glean impervious to factual distortions, erroneous summaries, or miscomprehensions of basic queries?
The possibility exists; however, this author didn’t undertake firsthand testing of Glean. Nonetheless, Jain, while coy on Glean’s error rates, underscored the myriad safeguards in place to bolster the platform’s reliability. These include a model trained on customer data to acclimatize to industry-specific vernacular and the flexibility for clients to toggle between various open-source GenAI models, tailoring Glean’s core functionality.
“AI assistants must furnish personalized outcomes predicated on the searcher’s profile—role, responsibilities, managerial hierarchy, ongoing projects, and collaborative ties all factor into relevance,” Jain elucidated. “Glean customizes a model for each client, ensuring highly personalized outcomes for every employee.”
Moreover, Glean deploys RAG (Retrieval-Augmented Generation), a prevalent technique augmenting GenAI’s grounding by drawing on external knowledge sources to enhance performance. Jain asserts that every response furnished by Glean is “fully traceable” to its origin.
“Glean can proactively suggest documents pertinent to daily tasks by learning from past user interactions,” Jain elaborated. “It delivers a seamless integration of an intricate AI ‘ecosystem,’ boasting over 100 connectors.”
Glean adopts a subscription-based revenue model, levying a monthly fee per user based on annual contracts.
Despite contending against formidable rivals like Microsoft (notably Copilot), OpenAI (ChatGPT), and enterprise search incumbents such as Coveo, Sinequa, and Lucidworks, Jain attests to Glean’s robust performance, with annual recurring revenue nearly quadrupling over the past year.
This narrative counters the prevailing notion that corporations, rather than fully embracing GenAI, remain cautious and sluggish in its enterprise-wide deployment.
According to a December 2023 survey by Convrg.io, a mere 10% of organizations had transitioned GenAI solutions to production by 2023’s end. The majority of solutions languished in the research and testing phases, hinting at the struggle to identify commercially viable GenAI applications.
However, Glean’s financial fortitude—coupled with a clientele spanning 200 firms, including Duolingo, Grammarly, and Sony—has captivated investors’ attention.
Glean recently announced a $200 million Series D funding round, co-led by Kleiner Perkins and Lightspeed Venture Partners, with a bevy of prominent backers participating. Kleiner Perkins’ Mamoon Hamid expressed unwavering confidence in Glean’s potential, reiterating his firm’s commitment since the Series A round in 2019.
Jain affirms that this fresh infusion of capital, catapulting Glean’s total funding to ~$360 million and valuing the startup at $2.2 billion, will fuel expansion across all fronts. The Palo Alto-based company, presently boasting ~300 employees, aims to bolster its product offerings and augment its go-to-market strategies.
“Glean continues to witness robust demand, particularly from enterprises navigating the prerequisites for GenAI integration,” Jain declared. “We’ve maintained judicious hiring and expenditure practices, with recent staffing increases geared towards meeting escalating customer demands.“
Conclusion:
The success and rapid growth of Glean underscore a burgeoning market demand for AI-driven solutions that streamline data accessibility and enhance productivity within enterprises. Despite initial hesitations surrounding privacy and reliability, Glean’s robust performance and substantial funding signal a shift towards widespread adoption of GenAI platforms in corporate environments, reshaping the landscape of enterprise knowledge management.
