AI Systems Engineering
Model evaluation, training data, retrieval, and the controls required to operate AI systems in production.
AI systems fail differently from conventional software — behavior is a distribution, not a binary, and it can drift as the data and usage underneath it change.
Evaluation, retrieval, guardrails, and monitoring aren't peripheral. They're the engineering layer that makes model behavior testable and supportable in production, not just something that worked in a demo.
AI Systems Engineering often begins as a Diagnostic when an existing AI investment is underperforming on cost, accuracy, or reliability. It also takes the form of Build and Operate when a new AI capability must be designed, evaluated, and run in production from the outset. See how we engage