Enterprise AI has a knowledge management problem, not a context engineering problem. Most organizations build separate retrieval pipelines for each AI application, meaning different teams process the same documents, maintain redundant embeddings, and produce conflicting representations of identical business knowledge. The result: agents in the same company operate on different versions of the same facts.
The article proposes a four-layer architecture: Raw, Refined, Integrated, and Serving. Raw preserves original sources so pipelines can be rebuilt without relying on application-specific copies. Refined normalizes everything, from PDFs to Jira tickets to API responses, into governed knowledge objects with consistent metadata including document ID, version, permissions, and lineage. Integrated connects those objects through shared business identifiers and AI-based entity resolution, linking a product requirement doc, a Jira story, and a release note that all describe the same feature but share no explicit relationship. Serving publishes reusable representations optimized for specific AI applications.
The core argument is structural: enterprise data platforms already solved this problem for structured data by managing it once and sharing it across systems. Enterprise AI needs the same discipline applied to unstructured knowledge. Read the full piece for the detailed breakdown of how each layer handles versioning, permissions propagation, and the specific failure modes that emerge when agents pull from inconsistent knowledge pipelines at scale.
[READ ORIGINAL →]