Automation built around the workflow
Typical projects include data collection and processing, reporting pipelines, internal dashboards, infrastructure routines, and security-tool orchestration. Work starts by mapping inputs, decisions, failure modes and desired outcomes instead of adding AI where simpler software is sufficient.
AI where it adds value
When the workflow calls for it, I can evaluate or integrate language models, retrieval workflows, classification systems, and explainable machine learning. Data handling, permissions, prompt-injection risks, quality checks and fallback behavior are addressed explicitly.
Engineering handover
Implementations can include documented configuration, error logging, scheduled tasks, testing, and deployment guidance. Integration choices depend on your environment and access; I avoid promising specific productivity gains before measurement.