Machine learning expertise focuses on practical model integration rather than academic research. This means selecting the right model for the job, building reliable inference pipelines, and ensuring ML components operate smoothly within larger application architectures.

Retrieval-Augmented Generation (RAG) is a particular area of strength. This includes building embedding pipelines, configuring vector stores for optimal retrieval, implementing hybrid search strategies, and designing chunking approaches that preserve document semantics. GraphRAG extends this further by layering knowledge graph structures over traditional vector retrieval for richer contextual understanding.

Knowledge graph systems provide structured representations of domain knowledge that complement unstructured AI approaches. By combining graph-based reasoning with language model capabilities, these systems deliver more accurate, explainable, and auditable results than either approach alone.