DataPilot

Resources

Database documentation tools for modern SQL teams

Documentation quality directly impacts query speed, analyst onboarding, and trust in exports. DataPilot combines catalog browsing with AI generation and DDL apply to keep schema comments current.

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Supported databases: PostgreSQL, SQL Server, MySQL, Amazon Redshift. More databases are coming.

Why database documentation matters

  • Faster query authoring when column purpose is explicit.
  • Lower onboarding time for analysts and engineers.
  • Fewer semantic errors in dashboards and scheduled exports.
  • Clear lineage context when relationships are documented.

Manual vs AI-generated documentation

Manual comments are precise but hard to maintain at scale. AI generation is fast but only reliable with real schema context. DataPilot combines both: AI drafts with human review and controlled DDL apply.

How DataPilot documents schemas with AI

  1. Load schema context: tables, columns, data types, keys, indexes, and existing comments.
  2. Generate publication-grade documentation in Markdown for technical and business readers.
  3. Review proposed table and column comment deltas before committing any change.
  4. Apply approved comments using ALTER TABLE and COMMENT ON DDL statements.
AI assistant details Catalog explorer
Database catalog AI workspace for schema documentation
Catalog and schema context in the same workflow used for AI documentation