This article proposes capturing corrections, decisions, and useful workflows as shared company knowledge. The central idea is a learning loop: improvements discovered in one person’s AI workflow should become reusable context for the rest of the organization.
MotherDuck distinguishes semantic layers, context layers, and ontologies. Shared metric definitions, business context, and explicit relationships each contribute something different; choosing an architecture starts with identifying which of those problems an AI application actually needs to solve.
Femke Plantinga highlights the variety of architectures being described as a company brain. Slite’s accompanying research looks at components, maintenance costs, and build-versus-buy choices, helping turn a broad label into concrete design decisions.
Jacob Posel argues for company brains designed around agent use and connections to live business systems. A static collection of documents is only part of the picture; usefulness depends on whether an agent can access relevant, current information in its actual workflow.