Turning database metadata into an AI-ready semantic manifest

Metadata sync is only the first step

Reading names and types does not create an AI-ready model. Scope reflection, collect structural facts, profile data, confirm relationships and semantics, publish a versioned manifest, and test it before agents consume it.

BuildTable publicly covers connection, structure, field semantics, metric definitions, and permission governance. This article describes a general implementation method rather than undisclosed internals.

Scope reflection

Restrict sources, schemas, and tables before inspection. Collect types, nullability, keys, indexes, and explicit relationships, with timeout, cancellation, and resumable state.

Diff structural changes and keep technical facts separate from human business descriptions so synchronization does not overwrite governed meaning.

Add profiling facts

Null ratios, distinct ratios, ranges, common values, and time coverage help identify candidate keys, statuses, and anomalies. Push aggregation into the database and limit samples and execution time.

Do not send sensitive raw text into model context. Timestamp profiling results because distributions and enumerations change.

Review and publish the manifest

AI can propose descriptions or relationships, but owners must confirm cardinality, metric formulas, and business rules. Store source, owner, status, and version for every semantic item.

Run structural, relationship, metric, and natural-language tests before publication. Mark downstream impact and support rollback for breaking changes.

Acceptance checklist

Verify scoped synchronization, recovery, preservation of human notes, sensitive-field exclusion, cardinality tests, metric examples, correct selection across 20 questions, and traceable versions. Connection is the start; explainability and governance make data AI-ready.