DataBuck: Autonomous Data Remediation Platform

Every remediation action includes user approval workflows, complete rollback capabilities, and full audit trails. Our multi-agent system built on Model Context Protocol (MCP) ensures fixes are validated across multiple contexts before execution. You maintain full control over what gets fixed and when.

Yes, DataBuck can safely remediate production data with built-in safeguards including approval workflows, staging environments, and instant rollback capabilities. All changes are logged with complete audit trails for compliance and governance.

DataBuck processes remediation at scale—billions of records can be fixed in hours. Most enterprise workloads under 100 million records complete remediation in under 90 seconds. Our distributed multi-agent architecture scales to handle the largest data volumes.

DataBuck remediates missing records, duplicates, format inconsistencies, migration errors, master data conflicts, referential integrity issues, and more. Our specialized agents handle different problem types in parallel for comprehensive data fixing.

DataBuck uses specialized agents built on Model Context Protocol (MCP) that communicate securely to address different data quality problems. Migration agents fix mapping errors, MDM agents resolve duplicates, and validation agents ensure fixes meet business rules—all working in parallel for faster resolution.

Absolutely. DataBuck integrates natively with dbt, Airflow, Databricks Workflows, Azure Data Factory, and other orchestration tools. Remediation can be triggered programmatically via API, scheduled automatically, or executed as part of your data pipeline workflow.

Every remediation includes complete rollback capability. If a fix causes issues, you can instantly revert to the previous state with one click. All changes are versioned and tracked, ensuring you can always restore data to any previous state.

DataBuck's agentic AI analyzes your data patterns, business rules, and historical corrections to automatically learn how data should be fixed. The AI continuously improves from your feedback and approval decisions, becoming more accurate over time at proposing the right remediation actions.