Distributed Worker Orchestration

AI document processing involves computationally demanding tasks such as OCR, document understanding and question answering. Running them on a single server creates bottlenecks, limits scalability and makes it hard to use computing resources efficiently. As workloads grow, the business needs a dependable way to coordinate processing across machines and see their health without tying AI work to the central application.

We built a distributed worker layer that separates task execution from central application management. Remote workers register what they can do, including the AI tasks they support and their GPU or CPU resources. Automatic heartbeats report availability and resource metrics, and a central API keeps worker identities and watches their health against a configurable threshold. Jobs are claimed and run through a separate job interface, so AI models, tasks and hardware can change without rewiring the platform.

The aim was to make processing scalable, convenient and autonomous, and setting up the worker layer solved that problem. GPU and CPU machines join the same processing system without manual configuration, and the central team can see what is running and what is healthy. Because execution is separate from management, the platform can grow without depending on a single processing server.