carriers operate networks their tools don't understand
Telecom networks are among the most complex systems humans have ever built.
Present are millions of cells, thousands of network functions, and petabytes of data streaming every hour across RAN, core, and transport layers that span continents.
To manage this complexity, we built abstractions. Dashboards. KPIs. Alert hierarchies. They compress the network into disparate parts a human can hold in their head.
The problem: these abstractions are lossy by design. They drop information—because it's too hard to source, too hard to integrate, too fragmented across vendors and proprietary formats. The data that would tell you why something failed often never leaves the element that generated it.
As networks grew, expertise necessarily specialized. RAN teams, core teams, transport teams—each mastering their domain. But specialization created silos. The interfaces between domains became the hardest places to troubleshoot, because no single team owns them.
Knowledge is tribalized, such that the industry has become over-reliant on few key vendors, and beholden to them to manage their own network.1
We believe that inefficiency scales exponentially with the complexity of abstractions, and that telecom companies are among the most complex organizations in the world. Perhaps, this was an acceptable tradeoff when humans were the only option.
AI lets us stop abstracting. We can build systems that reason across the entire network—at full resolution, across every domain, from the data that never made it to the dashboard.
approach
We operate our own 5G lab infrastructure. We have direct integration with tier-1 carrier environments. We are vendor agnostic. We've spent years learning to source, normalize, and interpret the element-level data that never makes it to the OSS layer.
From this foundation, we build AI systems that can observe and reason about networks the way a senior engineer would—if they could hold the whole picture in their head at once.