AI-Enhanced Systems

Integrating AI into existing systems involves embedding model inference, retrieval-augmented generation, or classification pipelines into operational data flows and application logic. Architecture considerations include model selection, API vs. on-premise deployment, context window management, prompt versioning, and output validation. Data pipelines must support feature engineering, vector storage, embedding refresh cycles, and audit logging. Governance requirements address hallucination mitigation, human-in-the-loop controls, and model observability. Integration patterns connect AI outputs to FileMaker, Claris Connect, custom APIs, or downstream BI tooling without disrupting existing system behavior.