Applied AI developed a "Large Work Model" (LWM) to automate complex business process outsourcing (BPO) workflows, but lacked the MLOps infrastructure to deploy it. They partnered with Revela to productionize their research, centered on a massive 671 billion parameter DeepSeek R1 model. The goal was to generate entire, optimized business workflows from a simple user prompt in under five seconds.

The solution involved a multi-stage pipeline. First, a user query is processed to determine intent. Then, fine-tuned BGE-EN-ICL embedding models generate context-aware vector representations for four distinct semantic roles: workflow intention, input, output, and process. These embeddings are used to perform semantic search against a "Work Knowledge Graph," which stores domain-specific procedural knowledge. This graph was implemented using a hybrid strategy of Neo4j for persistent storage and Memgraph for high-speed, in-memory queries.

The retrieved graph context is then fed to the fine-tuned DeepSeek R1 model, which generates a structured workflow. An optimization service refines this workflow for cost and time. The entire system was deployed on a secure, air-gapped, multi-cluster Kubernetes architecture using high-performance H100 GPUs for training and inference. This infrastructure supports 50-100 inferences per pipeline run while meeting the strict sub-5 second latency requirement, successfully transforming a theoretical AI concept into a scalable, enterprise-grade product.