Telmai Accelerate your AI with trusted data.

What is Telmai?

Telmai is a data observability platform designed to enhance data quality and trust within Data Lake and Lakehouse environments, specifically aiming to support AI initiatives. It operates without data sampling and is architected to avoid unexpected surges in cloud costs, offering an open architecture that integrates natively with raw formats like Iceberg, Hudi, and Delta. The platform focuses on providing comprehensive data quality checks, validating every value before it's ingested into AI models, and automating data quality workflows within AI processes.

Telmai employs ML-driven anomaly detection on column values and business metrics across entire datasets. It helps maintain consistency across different data layers (bronze, silver, gold) and offers no-code analysis and reporting on data health metrics. The platform includes features for incident management, such as alerting, ticketing, investigation, and remediation workflows, ensuring data reliability for building data products. Security is emphasized, with industry-leading practices protecting user data.

Features

Open Architecture: No-code connection to Data Lake and Lakehouse, natively supporting formats like Iceberg, Hudi, and Delta.
Comprehensive Data Quality: Validate every value before AI model ingestion; automate and orchestrate DQ workflows.
ML-Driven Anomaly Detection: Detect anomalies on column values and business metrics without sampling.
Data Layer Consistency: Improve quality across bronze, silver, and gold data layers.
Data Health Analysis: No-code analysis and reporting on data health metrics for data lakes/lakehouses.
Incident Management: Includes alerting, ticketing, investigation, and remediation workflows.
Extensive Integrations: No-code, low-code integrations with over 250+ data sources.
Security Focused: Protects data with industry-leading security measures.

Use Cases

Ensuring data reliability for AI and ML models.
Monitoring data quality across large datasets in data lakes and lakehouses.
Automating data validation and anomaly detection workflows.
Maintaining data consistency across different processing layers (e.g., Medallion architecture).
Generating reports on data health and quality metrics.
Managing data incidents from detection to remediation.
Integrating data observability into existing data pipelines.