Built on Google for an open architecture ecosystem available on the marketplace and runs in your GCP account
Discover how Telmai enhances BigQuery data reliability through automated anomaly and drift detection.
Learn how Telmai is aiding companies in trusting their data and ensuring that their AI models produce effective results.
Data Reliability Layer Built For Your Google Cloud Stack
Built natively on Google Cloud, Telmai’s AI-driven data reliability layer proactively detects quality issues, anomalies, and drift across BigQuery, BigLake, GCS, Dataflow, and Pub/Sub. Whether it’s an AI agent acting on a data product or a data engineer triaging a pipeline, Telmai surfaces the right data quality context at the exact moment it matters.
MCP-Compliant Monitoring Across Your Data Pipeline
Telmai’s MCP-compliant reliability layer monitors data quality across every stage of your data pipeline and pushes trust signals to both users and AI agents before bad data reaches the systems that depend on it. Built for Google Cloud’s open architecture, Telmai integrates without disrupting your existing stack or adding compute overhead to your warehouse.
Data Accuracy at the Record Value Level
Telmai uses both ML and user-defined expectations to observe data values as well as metadata in BigQuery, providing higher accuracy and understanding of data that feeds analytics applications, advanced machine learning models, and data products.
Performance and Scale at Low TCO
Telmai’s petabyte-scale platform, built on Spark and Google Cloud Services (GCP) including Google Dataproc, decouples its data quality analysis and scoring from the underlying data warehouses and analytical databases, and provides customers with data quality monitoring capabilities without overloading and slowing down these operational systems or increasing their infrastructure costs.
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