Glean's AI-Powered Growth Fueled by Automated Network Infrastructure
Enterprise AI company Glean has deployed Nile's Network-as-a-Service (NaaS) platform to support its rapid expansion across multiple campuses. The move is significantly reducing the network management burden on the company's lean IT team.
Key Results
- Download speeds surged from approximately 28 Mbps to nearly 100 Mbps.
- Employee connectivity complaints have largely ceased, with support tickets dropping to near zero.
- 5 GHz channel utilization decreased from about 75% to 50%, a clear indicator of reduced wireless congestion.
Background
As Glean expanded to multiple campuses, the operational demands of a manually managed network became a bottleneck. The company's small IT team wanted to shift focus away from daily infrastructure issues and toward internal strategic priorities.
To achieve this, Glean adopted Nile's platform, which consolidates network management, security, and automated monitoring into a single service. A key feature of the system is its built-in Zero Trust security, which verifies access for every device and user rather than automatically trusting activity within the corporate network.
Statements
Sunil Agrawal, Chief Information Security Officer at Glean:
"Building an enterprise AI platform means every part of the business has to move with urgency and intention. Our infrastructure has to be as intelligent and autonomous as the products we build."
Pankaj Patel, CEO and Co-Founder of Nile:
"Companies building the future of AI need infrastructure that accelerates innovation rather than slowing it down."
Context
This deployment reflects a broader trend: as organizations scale employee access and increasingly rely on AI tools, traditional networking can become a major constraint. Infrastructure vendors are now positioning internal networks as a critical component of the AI stack.
Traditional networking often requires separate tools for performance, security, and troubleshooting. Subscription-based services like Nile aim to replace that fragmented model with a unified, automated approach.