Abstract: Apparatus and methods are disclosed for implementing a copilot as a network of microservices including specialized large language models (LLMs) or other trained machine learning (ML) tools. The microservice network architecture supports flexible, customizable, or dynamically determinable dataflow. Compared to much larger competing LLMs, comparable or superior performance is achieved for certain tasks, while significantly reducing hardware requirements and computation time. Disclosed examples incorporate microservices for expansion, retrieval, embedding, and evaluation, in addition to one or more core microservices. Optionally, intermodal I/O, multiple data repositories, competency qualification, or human feedback can be supported. Multiple core microservices can support varying client authorizations or cognitive functions. The disclosed architecture supports any major LLM use case and can be deployed on a single compute node with a single GPU.