Composable Canonicals Turn Tribal Data Knowledge into Versioned, AI‑Ready Models
DLT Hub proposes an AI‑native architecture that codifies domain knowledge into versioned canonical models. By coupling raw data profiling with LLM‑driven ontology bootstrapping, teams can create stable identifiers that bridge disparate systems—e.g., HubSpot, Slack, Luma—enabling agents to reason across the stack. This reduces the central data team’s knowledge gap and cuts integration overhead.