Telmai is an AI-based data quality and observability platform built for open architecture and modern data workloads. The platform enables organizations to continuously validate, monitor, and improve the quality of their data at scale, without the need for data sampling or risking blind spots in data integrity.
Telmai helps businesses process their entire datasets—identifying column-level issues and automatically excluding problematic data from AI and analytics workloads. This proactive approach saves time and resources by ensuring only high-quality data drives downstream processes.
A key differentiator for Telmai is its open architecture. The solution natively integrates with cloud storage systems like Amazon S3, Google Cloud Storage (GCS), and Azure Data Lake Storage (ADLS), supporting open data formats such as Apache Iceberg, Delta Lake, and Hudi. This makes it particularly well-suited for organizations leveraging modern, open data lakehouses and AI-driven pipelines.
Designed as a metric monitoring system, Telmai delivers high performance and elastic scalability at a low cloud cost. The platform operates entirely within a customer’s own cloud environment (VPC), ensuring data privacy and security—no data is copied or moved outside the client’s cloud storage.
Who Uses Telmai?
Telmai serves data-driven enterprises looking to ensure data quality and observability across large, complex datasets. Notable customers include Datastax, Clearbit, and Merkle, reflecting its focus on organizations with advanced analytics and AI requirements.
How Was Telmai Started?
Telmai was founded by professionals with deep expertise in data and AI products: Mona Rakibe (Co-founder and CEO), Harlow Ward (CTO of Clearbit), and Alejandra Cabrera (Data Product Manager at Clearbit). Their combined background in data product management and technology leadership shapes the platform’s focus on scalable, enterprise-grade data quality solutions. More about their journey can be found on Telmai's About page.
What Makes Telmai Different?
- Processes entire datasets without sampling, avoiding blind spots.
- Native support for open architectures and modern data formats.
- Low cloud cost through efficient metric monitoring and elastic scaling.
- Fully managed, zero-copy operation within the customer’s own cloud environment for enhanced security.
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