Telmai offers a centralized data observability platform that enables data teams to perform machine learning-driven anomaly detection and validate data quality before ingestion into AI models. The platform integrates with data lakes and lakehouses, providing real-time insights and incident management to ensure data reliability across all processing layers.
Funding
$5.6M 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.
4VFounders
Product
Problem
Data lakes and lakehouses often suffer from data quality issues that can compromise the reliability of AI models and analytics. Traditional data quality monitoring solutions can be complex, costly, and may rely on sampling, leading to incomplete or delayed detection of data anomalies. This can result in untrustworthy data being ingested into AI pipelines, leading to inaccurate insights and flawed decision-making.
Solution
Telmai offers a centralized data observability platform that leverages machine learning to provide comprehensive data quality monitoring within data lakes and lakehouses. The platform performs anomaly detection on column values and business metrics without sampling, ensuring that every data point is validated before being used in AI models. Telmai integrates natively with raw data formats like Iceberg, Hudi, and Delta, providing real-time insights and incident management across bronze, silver, and gold layers. By automating data quality workflows and providing no-code analysis of data health metrics, Telmai helps data teams ensure data reliability, improve data layer consistency, and accelerate AI initiatives.
Target Audience
Telmai is designed for data engineers, data scientists, and business users who need to ensure the quality and reliability of data within their data lakes and lakehouses, particularly for AI and analytics applications.
Features
- ML-driven anomaly detection on column values and business metrics without data sampling
- No-code connection to data lakes and lakehouses, natively supporting raw formats like Iceberg, Hudi, and Delta
- Automated data quality validation and orchestration of data quality workflows within AI workloads
- Data layer consistency checks to improve data quality across bronze, silver, and gold layers
- Automated data health analysis and reporting on data health metrics
- Incident management features including alerting, ticketing, investigation, and remediation workflows
- Open architecture that supports integration with over 250 data sources
- Role-based access control and industry-leading security measures to protect data