Telmai's latest product feature updates enhance data quality workflows, enabling enterprises to accelerate AI adoption while ensuring data reliability and compliance across heterogeneous cloud environments.

SAN FRANCISCO, Dec. 11, 2024 /PRNewswire/ -- Telmai, the AI-powered data quality company, today announced significant enhancements to its enterprise data quality platform, introducing automated workflows designed to accelerate AI adoption. The new capabilities enable organizations to automatically monitor, validate, and optimize data quality across their AI implementations while ensuring regulatory compliance and data reliability at scale.

As enterprises increasingly rely on AI to power decision-making and innovation, the complexities of managing diverse and vast data ecosystems often hinder success. Inconsistent data quality, manual validation processes, and the risk of sensitive data exposure create roadblocks that slow AI adoption and impact outcomes.

Telmai addresses these challenges head-on with updates that streamline operations, enhance accuracy, and scale seamlessly across systems.

Native integration support for Open table formats to power AI workloads

Telmai's integration with Apache Trino expands support for open table formats like Apache Iceberg and Delta Lake by providing native data quality monitoring and observability capabilities. These formats are essential for building scalable, modern data architectures that support advanced AI applications. They enable seamless querying, schema evolution, and efficient scalability, which is critical for handling large and dynamic datasets in AI pipelines. Telmai natively supports column-level data quality monitoring without pre-processing or sampling your data by embedding automated data quality checks directly within these open formats. This minimizes operational overhead for users and accelerates AI adoption while ensuring high fidelity in data-driven decision-making.

Automated detection and prevention of exposure of sensitive data in AI workloads

Telmai introduces automation for detecting and preventing sensitive data exposure in AI workloads, addressing a critical need for enterprises operating in regulated environments. Through AI-powered pattern recognition, organizations can automatically identify and protect PII data, such as credit card numbers and social security details, across their AI training datasets, ensuring compliance while accelerating development cycles.

"With these innovations, Telmai is simplifying the complexities of data quality, enabling enterprises to focus on leveraging AI for transformative growth," said Maxim Lukichev, co-founder and chief technology officer at Telmai. "Our automated workflows, open format integrations, and advanced automated workflows provide the foundation for scalable, accurate, and trustworthy AI systems."

Efficient metadata scanning capabilities that reduce resource overhead

Telmai's metadata-only scanning capabilities dramatically reduce the computational overhead typically associated with data quality monitoring. Users have the flexibility to perform complete column-level data checks for critical data elements or ensure metadata validation across all data assets. This approach enables comprehensive quality controls across massive datasets without impacting performance or adding significant costs.

Don't let data quality challenges slow your AI adoption. Take the first step towards building reliable AI pipelines. Click here to see how Telmai ensures data accuracy and reliability at scale and achieves AI success.

About Telmai

Telmai is a data observability platform company that enables enterprise data owners to monitor and detect real-time data issues. The platform leverages AI to monitor all data passing through the data pipeline before entering the data warehouse, protecting downstream systems and analytics used for decision-making. Telmai's real-time architecture supports anomaly detection closest to data sources and works over complex data types with native support for nested and multi-valued attributes. For more information, please visit Telmai.

Contact: Steve Carman | [email protected]

SOURCE Telmai