Telmai
AI-based data observability for open lake and lakehouse architectures
About Telmai
Telmai provides data observability that validates structured, semi-structured and unstructured data as it lands in data lakes and lakehouses. It exposes data quality metadata to catalogs and AI agents, supports natural-language setup and uses a set of reliability agents for diagnosis and routing.
Common use cases
- Monitor lakehouse data
- Feed quality signals to catalogs
- Triage data incidents
Products and services
Telmai Platform
Data observability, anomaly detection and incident management for open architectures.
Key features
- Continuous validation
- Anomaly detection
- Incident diagnosis
- Natural-language configuration
- Metadata via MCP
- Data health analysis
Integrations
- Databricks
- Snowflake
- Apache Iceberg
- Delta Lake
- Amazon S3
Telmai alternatives
Monte CarloData and AI observability platform that detects data incidentsAnomaloAutomated data quality monitoring for structured and unstructured data
BigeyeData observability and quality monitoring for enterprise data pipelines
SodaData quality and observability with data contracts and AI-assisted checks
FAQs
What architectures does it target?
Data lakes and lakehouses with open table formats.
Does it support AI agents?
It exposes quality metadata to agents and catalogs, including via MCP.
Where is it registered?
A San Francisco address is listed on its site.
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