Enterprise AI projects stall because the meaning behind warehouse tables, from entities and keys to metrics and expert rules, is neither written down nor recoverable from schema or query history. Ekai closes that gap for enterprise data teams by profiling the warehouse in place, capturing definitions from the experts who hold them, and inferring entities, keys, and relationships with evidence. It then generates dbt models, tests, documentation, a glossary, metrics, and lineage, reconciling them inside the customer warehouse before publishing.
Unlike hand-authored dbt semantic layers and query-history mining, which infer meaning from what analysts happened to query, Ekai verifies its models against business facts experts state before publishing. It also avoids the months-long manual semantic-modeling engagements that leave the model stale on delivery, because the layer is generated and reconciled in place and stays warehouse-neutral and private, returning standard dbt, YAML, and JSON the customer owns.
