In previous editions, we've discussed the necessity of an AI-native platform like AgentOS for enterprises to successfully implement AI solutions. We've also covered the core capabilities of AgentOS, including the Workflow Engine. In this blog, we'll focus specifically on AgentOS Search.
Over the past two decades, various studies by IDC have revealed that employees spend about 20-30% of their time searching for relevant information needed for their tasks. According to McKinsey & Company, employees spend an average of 1.8 hours daily searching for information, which translates to nearly 10 hours of lost productivity per week per employee. Additionally, interaction workers spend approximately 28 percent of their workweek managing emails and 20 percent looking for internal information or colleagues who can assist with specific tasks. However, this search process is often so cumbersome and disjointed across their tools/ apps that it significantly hampers worker efficiency on a daily basis.
Search engines address these inefficiencies by transforming how employees access and utilize organizational knowledge. By converting this knowledge into searchable content, we can reduce the time spent searching for information by up to 30 percent, fostering faster, more efficient, and more effective collaboration. The benefits extend across various industries, enabling companies to capture value through improved internal communication, collaboration, and quicker decision-making processes.
But your tools have search capabilities
Despite the search functionalities in platforms like Jira, Salesforce, GitHub, Notion, and HubSpot, there's a critical need for a dedicated "Search for Enterprises" solution. API integrations attempt to centralize data but fall short due to the lack of interconnected data relationships. Even when data is brought together, the inability to establish meaningful connections between different data sources renders it nearly useless. According to IDC, inefficient search functionalities cost businesses up to $2.5 million annually in lost productivity.
The search functionalities within individual applications are designed for their specific ecosystems and fail to provide a unified search experience across multiple platforms. This fragmentation forces employees to switch between tools, disrupting their workflow. According to HBR report, 28% of company time is spent on reading and answering to internal & external communication, further highlighting the need for efficient search solutions. Legacy search solutions add to the problem with their lack of integration with each other, unable to keep up with the growing data and complexity of modern enterprises.
Making a case for role aware search
Implementing robust permissions and authorizations in "Enterprise Search" is crucial for maintaining data security and compliance. With information scattered across platforms like Jira, Salesforce, GitHub, Notion, and HubSpot, ensuring users access only authorized data prevents breaches. By leveraging role-based access control (RBAC) and attribute-based access control (ABAC), enterprises can fine-tune who sees what data, reducing the risk of insider threats and ensuring compliance with data protection regulations like GDPR and HIPAA. This tailored access protects sensitive information and enhances productivity by providing secure, relevant search results. This not only secures data but also boosts efficiency, reducing time spent searching.
AgentOS
DevRev is looking to address this need by a comprehensive knowledge graph to understand both structured and unstructured data across various platforms. As a system of record for critical business information, DevRev is uniquely positioned to provide this capability. Ensuring that search results are not only relevant but tailored to each user's specific role and needs within the organization. This means that users only see results they're authorized to access, maintaining data security and compliance while streamlining the search process. With our knowledge graph you can see meaningful connections between disparate data sources, providing a coherent view of information that was previously siloed across different platforms. This interconnected approach allows to find contextually relevant search results, significantly reducing the time employees spend searching for information.
DevRev's commitment to AgentOS further enhances our search capabilities. By investing in an AI-native platform, we're not just providing a search tool, but an intelligent assistant capable of understanding and answering complex user queries. This functionality, already proven successful with our Turing AI Answers feature for public documentation, is now being extended to cover a broader range of enterprise data. As businesses continue to generate more data across various platforms, DevRev's search solution stands as a necessary tool for maintaining efficiency and competitiveness in the modern enterprise landscape.
Enterprise search economics
DevRev's approach to enterprise search goes beyond just technical innovation - we're fundamentally rethinking how organizations access and pay for search capabilities across their tools.
Eliminating redundant license costs
Traditional enterprise search solutions often require maintaining separate read-only licenses across multiple SaaS applications just to enable search functionality. DevRev eliminates this overhead through our intelligent Knowledge Graph architecture. By caching and rendering all searchable assets directly within our Knowledge Graph, we remove the need for additional read-only licenses while maintaining complete data security and access controls. This can lead to significant cost savings, especially for large enterprises managing multiple SaaS tools.
Usage-based economics
We recognize that organizations are still adapting to AI-powered search capabilities. Rather than forcing rigid subscription commitments, DevRev offers consumption-based licensing tied to Monthly Active Users (MAU). This flexible model allows organizations to:
- Scale costs directly with actual usage and value delivered
- Avoid paying for unused search capacity
- Gradually expand adoption based on proven ROI
- Maintain predictable costs while experimenting with new search use cases
Extensible through community innovation
DevRev's enterprise search capabilities become even more powerful through our open marketplace approach. Using our ADaaS (Airdrop-as-a-Service), developers can create custom connectors to integrate additional data sources into the DevRev Knowledge Graph. This means:
- Organizations can extend search capabilities to custom and legacy systems
- The developer community can contribute new connectors and innovations
- Search coverage can grow organically based on enterprise needs
- Faster time-to-value for specialized use cases
As both a system of record and an AI-native platform, DevRev offers unique advantages for enterprise search:
- Our Knowledge Graph understands both structured and unstructured data relationships
- Native role-based access controls ensure security without sacrificing usability
- Consumption-based pricing aligns costs with delivered value
- Community-driven extensibility future-proofs search capabilities
- Unified search reduces cognitive load while maintaining context across tools
This is part 5 in a series of blogs, case- study, white-paper and podcasts on “AgentOS & its capabilities”. Stay tuned…
