Explainable data quality, fully automated

Traditional data quality tools need you to write and maintain validation rules, such as 'Revenue must be > 0', which means anticipating every possible issue. Anode learns your data's patterns and catches anomalies you didn't expect, and every alert comes with a clear explanation rather than a failed-rule count. For example: 'Product_Code ABC-123 appears 847 times, but codes in this category typically appear 50-100 times. That is 8.47x the normal frequency, which suggests duplication or a processing error.'

Anode detects 6 classes of anomalies: (1) Data type anomalies, values that don't match the column's data type, such as text in a numeric column; (2) Abnormal values, values that don't fit the column's meaning, such as a non-country in a country column; (3) Categorical anomalies, proportionally rare values; (4) Numeric anomalies, numbers outside the normal range; (5) Typos, spelling mistakes in English text; and (6) Blank values, missing data in columns. Each finding includes the affected rows, a confidence score and a plain-English explanation.

No. Anode is built for data teams and business users alike. Connect your data sources and Anode starts profiling and monitoring them. Explanations are in plain English, not technical jargon.

Agentic AI for data quality means AI that monitors, detects and explains data issues without constant human supervision. Anode scans your data, identifies the 6 classes of anomalies above and explains why each issue was flagged.

Yes, explainability is Anode's core strength. Every alert comes with a natural-language explanation, a confidence score and the specific values involved, so your team can trust the finding and act on it quickly.

Anode gives every row a quality score from 0 to 1, where 1 means no issues found. The score considers completeness, accuracy, consistency and timeliness. A dashboard shows overall data health, and you can drill down to the records with problems.

Both. For real-time use cases, Anode monitors data streams and alerts you when quality drops. For batch scenarios, it validates data before it is loaded into your systems, so bad data doesn't reach your analytics.

Anode integrates with major databases (PostgreSQL, MySQL, SQL Server, Oracle), cloud data warehouses (Snowflake, BigQuery, Redshift, Databricks), data lakes (S3, Azure Data Lake) and streaming platforms (Kafka, Kinesis). A Snowflake Native App is available on request.

Anode keeps historical quality metrics and audit trails that support governance work and compliance audits under regulations like GDPR and HIPAA. Enterprise features include role-based access control and encrypted data processing. Codygon does not hold third-party compliance certifications today; as an AI-first company we build these controls into the product, configure each deployment to the standards you must meet, and can pursue a specific certification if your organization requires it.

Anode learns from your feedback, so detection improves over time on your data. Because every alert explains its reasoning, your team can quickly tell a real problem from an expected pattern and mark it accordingly.

Anode starts reporting on a new data source within minutes: it profiles your data, learns normal patterns and starts detecting issues, with no rules to write. A full enterprise deployment typically takes 1-2 weeks, including team training and workflow integration. These are our estimates.

Yes. Free trials are available on request.