AI Engineering

AI Engineering bridges the gap between raw data and reliable, autonomous intelligence. By formalizing complex research knowledge and building strict validation logic, this pillar transforms unpredictable generative models into trustworthy, auditable research partners. The focus is on creating semantic frameworks that allow AI systems to reason, navigate domain-specific literature, and execute multi-step analytical workflows without hallucination.

• Enterprise Knowledge Graphs: We design and deploy formal ontologies and domain-specific knowledge graphs to explicitly map complex datasets, variable relationships, and institutional workflows.

• Advanced Retrieval-Augmented Generation (RAG): We architect high-performance RAG and hybrid GraphRAG pipelines that securely ground large language models in verified, structured research data.

• Autonomous Multi-Agent Frameworks: We build intelligent multi-agent orchestration systems capable of executing complex, multi-step scientific and operational workflows.

• Knowledge Extraction & Entity Linking: We implement automated, multimodal pipelines utilizing Vision-Language Models to rapidly transform unstructured text, documents, and media into structured, graph-ready assets.

• Deterministic Guardrailing & Validation: We build automated semantic validation layers, constraint queries, and governance guardrails to ensure AI outputs are reproducible, auditable, and safe.

For more information, or to request services, please contact Caleb Reinking at creinkin@nd.edu