Industrial companies face challenges with operational data quality, including issues such as inaccurate, missing, or outdated data. These data quality problems can lead to unreliable analytics, flawed predictive models, and increased operational risks.

APERIO AI provides a machine-learning-powered platform that ensures the accuracy and reliability of operational data for industrial organizations. The platform automatically identifies and alerts users to data anomalies, such as bad, missing, or stale data, without requiring manual configuration or custom rules. By improving data quality from its source to end-user applications, APERIO enhances the performance of analytics, predictive models, and AI initiatives. This leads to reduced operator errors, minimized unscheduled downtime, and improved asset health, while also enabling better metrics for benchmarking and sustainability reporting.

Automated anomaly detection using unsupervised machine learning to identify data quality issues without manual rule definition.

Connectors to millions of data streams for various equipment and sensor types, eliminating the need for custom configurations.

Data quality measurement and tracking through smart workflows, root cause analysis, and pattern recognition.

Real-time alerts and notifications for immediate action on identified data anomalies.

Comprehensive data observability reports to quantify and prioritize data quality issues.

Role-based access control to ensure data security and compliance.

APERIO AI targets industrial companies across sectors such as Oil & Gas, Chemicals, Power, Mining, Pulp & Paper, Pharma, and Manufacturing. Specific users include data scientists, operations managers, and reliability engineers.