What to Look for in a Context Layer

A context layer is presented as infrastructure that supplies AI applications with live, accurate, permission-filtered data from enterprise systems such as CRM, ERP, HRIS, warehouses, and ticketing platforms, positioned between AI applications and underlying data sources. Unlike retrieval-augmented generation, which is suited to unstructured documents indexed periodically, a context layer is intended for current, structured operational data that may require cross-system joins, semantic interpretation, and transaction-level access controls; the two approaches are described as complementary in enterprise deployments. The proposed evaluation framework centers on source coverage, including custom fields and on-premises connectivity; schema intelligence and semantic resolution for identifying relevant fields, business definitions, and relationships; enforcement of each requester’s source-system permissions with auditable query logs; and measured outcomes such as grounded accuracy, token efficiency, and latency. The piece argues that enforcing permissions at the data source is essential for security and compliance, while noting that organizations should request benchmarks reflecting their own workloads. It concludes by describing CData Connect AI as a product designed around these capabilities and cites company testing and external research to illustrate the market’s growing focus on governed, hybrid retrieval for agentic AI.

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