Auditing AI: Beyond the Prompt

Generating text with AI is straightforward; relying on its calculations, contract interpretations, and market summaries is where business risk begins. Language models process vast volumes of unstructured data rapidly, but they generate plausible fictions, smooth over non-linear costs, and fail at unassisted arithmetic.

This group focuses on the mechanics of error control: treating AI strictly as a high-throughput computational engine, auditing its outputs, and separating automated data ingestion from deterministic verification.

We host structured Zoom sessions and working discussions focused on practical business data, analytical workflows (including R/Python verification), and empirical reality checks. No vendor pitches, no generic prompt lists—strictly operational data integrity.