The Company

Guru Technologies, Inc. was founded by CTO Mitchell Stewart and CEO Rick Nucci in 2013, and the firm is headquartered in Philadelphia, Pennsylvania with a second office in San Francisco, California. Currently on Series C funding, Guru has raised $70.7 million from investors like Accel, FirstMark, Emergence, and Thrive Capital. As of April 2021, Guru has over 1,000 customers including Shopify, GitHub, and Spotify.

The Technology

We position Guru as a Knowledge Network, or Knowledge Automation Platform, providing a single source of truth for corporate information. However, it is typically deployed in three main situations: to support customer service by providing accurate product information, to streamline employee onboarding, and to facilitate internal communications.

What this means in practice is that information fragments are stored in a single cloud database run by Guru. Once stored there, the information is automatically analyzed for relevant content and, over time, usage patterns associated with the various information fragments. In turn, as the system leverages analytics and machine learning (ML), it can suggest relevant content in context to the end user.

The information fragments are captured from wikis, intranets, and collaboration systems. Users can also directly upload information in the form of spreadsheets, web browsers, documents, text, images, and videos. The information, once analyzed, is surfaced via a web browser or through integrations with applications like Zendesk, Slack, or Confluence Cloud. Suggestions can also be surfaced in chat tools or email. Of particular interest here is Guru’s ability to monitor voice interactions and surface relevant suggestions based on those conversations. It is also possible to map knowledge so that it surfaces in specific teams or webpages.

What we have here, then, is Knowledge Management reinvented. The traditional tools of KM – capturing knowledge, managing it, and making it available – are supplemented by strong push capabilities and strong reporting based on usage patterns.
Just like traditional KM tools, Guru makes use of a search capability (based on Elastic Search). Over time, through the use of ML, it adapts to further influence the sorting of search results based on the information the system has learned about you as a user, your rights to access, and your interests.

From an end-user perspective, Guru leverages the concept of “cards” for sorting, accessing, and managing knowledge sources (see Figure 1). Cards can be tagged and verified, synced to an outside source, and sorted by topic, teams, or boards. The combination of basically manually managed cards with AI-supported suggestions seems a solid one.

Finally, it is important to note that Guru is used less as a replacement system and more as an augmentation layer on top of your existing systems, bringing relevant, accurate, and timely knowledge into employees’ workflows. So, rather than ripping and replacing the information silos within a traditional intranet or within employee communication and productivity systems like Slack, Teams, or Salesforce, it integrates with what is already there. Hence, it has developed a strong set of over 30 connectors and APIs to commonly used business systems.