Metasecure AI

Key Features :

Agent Data Quality provides automated data quality governance through LLM-powered agents and a modular, scalable architecture on AWS. Its multi-agent system—comprising Check Creation, Monitoring, and Reporting Agents—enables intelligent rule generation, real-time validations, and contextual summaries. The platform uses Amazon Bedrock for natural language interaction, while the validation engine leverages Python and Jinja2 templates for reusable rules. Results and metadata are stored securely in Amazon RDS and S3, with visual insights delivered via Amazon QuickSight dashboards. Security is built-in, with AWS IAM for access control, VPC for network isolation, TLS for encrypted data transfer, and CloudWatch for observability and alerting. Agent pods run as stateless services on Amazon EKS, auto-scaled via the Horizontal Pod Autoscaler (HPA) for workload efficiency.

Use Cases :

Agent Data Quality supports mission-critical data quality operations across diverse industries. In Retail, it ensures product catalog accuracy by validating syncs from multiple data sources. In Finance, it validates transactional data and monitors anomalies for fraud detection. Manufacturing companies use it to assess the quality of real-time IoT and sensor data. Logistics firms track SLA adherence and detect inconsistencies in warehouse or shipment data. The solution is also valuable for Regulatory Reporting, helping teams generate explainable, auditable data quality summaries aligned with compliance frameworks.

Target Users:

Agent Data Quality is purpose-built for enterprise users responsible for data governance and trust. Data Engineers use it to automate validation workflows. Data Stewards define and manage quality rules using an intuitive interface. Compliance Officers rely on audit logs and summaries for regulatory reporting. Governance Teams track the state of data quality through dashboards, while Business Analysts benefit from natural language insights that accelerate issue detection and resolution across datasets.

Technical Requirements :

To deploy Agent Data Quality, enterprises need a Kubernetes environment such as Amazon EKS with IAM roles, VPC configuration, and TLS-enabled ingress. The solution requires access to Amazon RDS (PostgreSQL) for metadata storage, Amazon S3 for results, and Amazon Bedrock for LLM inference. It also integrates with Amazon QuickSight and CloudWatch for visualization and monitoring. Operational knowledge of Kubernetes and Helm is recommended for managing deployments, scaling agents, and maintaining GitOps pipelines.

Deployment Architecture:

Agent Data Quality is deployed as a containerized microservice solution on Amazon EKS. The architecture includes distributed agents for check generation, monitoring, and reporting. The rule engine, based on Jinja2 and Python, is orchestrated via Airflow triggers or cronjobs. Results and rule history are stored in Amazon RDS and S3, while dashboards are generated using Amazon QuickSight. TLS is enforced for secure data transfer, and encryption at rest is enabled through AWS KMS. IAM-based role access and VPC-based network isolation ensure secure multi-environment operation. Agent workloads are horizontally auto-scaled using Kubernetes HPA, and operational metrics are tracked via CloudWatch.

Benefits:

Agent Data Quality empowers organizations to move from manual, reactive rule enforcement to automated, intelligent data quality governance. It reduces validation time by over 70% and accelerates data-driven decision-making with explainable summaries. The platform enhances compliance through full audit trails and aligns with enterprise security standards via IAM, VPC, and KMS integration. Its modular design enables multi-tenant and cross-environment deployments, making it ideal for data-centric enterprises operating at scale. By integrating AI with governance, Agent Data Quality delivers continuous trust and transparency across the data lifecycle.