Where the systems go to work
How our schedulers, brokers and telemetry are deployed in real operations, from GPU fleet optimization and agentless observability to governed autonomous AI agents.
Fleet Observability & Infrastructure Simplification
The challenge
Engineering teams often run multiple conflicting monitoring agents across thousands of servers, resulting in memory overhead, version drift, and slow incident triage.
Our approach
We replace bulky client daemons with agentless SSH probes that gather full kernel and GPU counters in one second, paired with automated root-cause analysis runbooks.
Outcomes
Governed AI Agent Execution & Spend Controls
The challenge
Unrestricted autonomous AI agents can leak root API tokens, trigger unbounded inference loops, and generate unexpected cloud bills within hours.
Our approach
We put an in-app proxy between agents and model APIs to strip root credentials, inject Ed25519-signed session tokens, and return HTTP 429 when monthly dollar ceilings are reached.
Outcomes
Heterogeneous GPU Cluster Scheduling
The challenge
Most cluster tools only support one GPU brand, leaving AMD, Apple Silicon, and Intel hardware idle while teams wait for expensive cloud instances.
Our approach
We deploy a single-binary gossip scheduler that discovers and balances tasks across mixed NVIDIA CUDA, AMD ROCm, Apple Metal, and Intel Arc hardware without a central master node.
Outcomes
Offline-First Field Platforms & Labor Scheduling
The challenge
Field teams working in remote or low-connectivity zones frequently lose submission data, while dispatchers struggle to cover last-minute shift shortages.
Our approach
We build offline-first web apps that store records in local IndexedDB outboxes and sync automatically when reconnected, paired with an automated shortage forecasting engine.