AI-Powered Data Reconciliation & Validation Platform

Configure powerful validation rules with multiple rule types to enforce data quality at every stage.

• Null Check — Detect missing or empty values
• Range Validation — Ensure values fall within expected bounds
• Regex Patterns — Validate data format with custom patterns
• Referential Integrity — Cross-table reference validation
• Uniqueness Constraints — Enforce unique value requirements
• Custom SQL Rules — Write custom validation logic in SQL
• Cross-Dataset Validation — Compare rules across data sources

Automatically profile your data to understand its shape, quality, and patterns before reconciliation.

• Column Statistics — Count, nulls, distinct values, distribution
• Anomaly Detection — Statistical outlier identification
• Schema Drift Detection — Automatic schema change tracking
• Data Categorization — Completeness, accuracy, consistency
• Severity Levels — Critical, High, Medium, Low classification
• Tolerance Controls — Absolute, percentage, or none
• Rule Library — Reusable library of validation rules

Build complex reconciliation workflows with a drag-and-drop node-based editor.

• Recon Source Nodes — Extract data from source systems
• Recon Target Nodes — Extract data from target systems
• Compare Nodes — Run comparison logic
• Validate Nodes — Post-comparison validation
• Action Nodes — Trigger notifications and integrations
• Library Nodes — Reusable data transformation blocks

Run reconciliations on your schedule with enterprise-grade orchestration.

• Cron Scheduling — Flexible cron-based job scheduling
• Airflow Integration — Native Apache Airflow DAG generation
• Event-Driven — Trigger recon on data arrival events
• Recon Templates — Reusable job templates for fast setup
• DBT Integration — Process automation with dbt transformations
• Celery Workers — Distributed task execution at scale