Build self-reliant teams with automated data quality
Validate every data value before ingesting into AI-model, automate and orchestrate data quality workflows within AI-workloads
Visibility into the health of the entire data
- Get automated reports on Data Quality KPIs such as completeness, correctness, uniqueness, timeliness, validity, and accuracy without any setup
- A no-code interface to enable users to define and manage flexible thresholds for custom data quality KPIs
- Monitor & assess the quality of data in your pipeline by comparing it with past trends
Business user-friendly rules and contracts
- Easy to use interactive no-code interface to build and manage data quality checks for your key tables
- Advanced configuration options to set up complex rules within a single table or across multiple tables
- Automate actions like alerts, DQ binning, circuit breaker, and remediation based on rule outcome
With well-governed data across your organization
- Effortlessly integrate with data catalog of choice
- Realtime data reliability insights and alerts within data catalogs
- REST API support to integrate into any Catalog system
Ensure teams trust your AI outcomes and not question it
- Orchestrate workflows to Identify suspicious data and segregate it for review
- Integrate with the ticketing system for review and remediations
- Notify impacted users whenever there’s a data outage
More features
Connect Datasources
Connect your datasource, or send data via REST, or load a local file
Analyze Data Health
Quickly identify and pinpoint data anomalies, errors, or inconsistencies
Alert
Telmai will learn your data and its trends and automatically alert on unexpected drifts
Recommendations
Telmai will finally advice you on next best actions for your data sets
Open Architecture
No-code connection to Data lake and Lakehouse –natively support raw formats like Iceberg, Hudi and Delta
Data Quality
Validate every value before ingesting into AI-model, automate and orchestrate DQ workflows in AI-workloads
Anomaly Detection
No sampling, ML-driven anomaly detection on column-values and business metrics
Cross-System Consistency
Improve quality across bronze, silver and gold layers embedded design patterns to stop bad data at bronze
Data Health Analysis
No code analysis and reporting of your data lake and lake house
Incident Management
Alerting, ticketing, investigation and remediation workflows