Inside the Connect AI Context Engine

CData Connect AI Gateway’s context engine is presented as a system for helping AI agents interpret enterprise questions accurately by combining source-system metadata, business semantic definitions, and unstructured organizational knowledge into a shared, editable context graph. It aims to resolve ambiguities such as metric definitions, fiscal calendars, source-specific terminology, joins, and reconciliation rules before an agent queries data, while also selecting an appropriate model and limiting retrieved data and tool calls. The engine can build context from CData connectors, existing semantic models and catalogs, documents, prompts, user feedback, and monitored conversations, with human review and access controls intended to govern updates. CData says this approach enables consistent definitions across AI tools and models, reduces token use, and improves accuracy; in its cited benchmark of 378 prompts, it reported 98.5% accuracy for Connect AI compared with 65–75% for other MCP approaches, alongside substantial reductions in tokens and tool calls. The context engine is available through an early-access program, with a live demonstration planned for the company’s Foundations 2026 event on November 5.

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