This session walks through a real‑world blueprint for building an agentic analytics system that runs entirely on your local machine, designed to analyze Blackbaud Raiser’s Edge NXT® data with a strong focus on data governance, speed, privacy, and cost control.

You’ll see an end‑to‑end architecture that incrementally syncs Raiser's Edge NXT® data via the SKY API into a locally encrypted SQL store, creating a secure and high‑performance foundation for downstream analysis. From there, the session demonstrates how to integrate local large language models (LLMs) into an agentic workflow to automate analysis, surface insights, and support decision‑making—without sending data to the cloud. Rather than focusing on theory, this is a practical, hands‑on look at guardrails, architecture patterns, and proven techniques for safely operationalizing local AI against sensitive fundraising data.

By the end of the session, you’ll understand how to build a secure local data cache using SKY API best practices, design a production‑ready agentic architecture with open‑source LLMs, and unlock advanced analytics and automation—without compromising governance or trust in your data.