APERIO AI employs machine learning to ensure the accuracy and reliability of operational data for industrial companies, addressing issues of bad, missing, or stale data. By automating the identification of data anomalies, APERIO enhances data quality for analytics and predictive models, ultimately reducing operational risks and improving asset health.
Funding
$9M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Industrial companies face challenges with operational data quality, including issues such as inaccurate, missing, or outdated data.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.
Solution
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.
Target Audience
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.
Features
- 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.