Telmai

Telmai is an AI-driven data observability platform built for open lakehouse architectures. It continuously validates structured, semi-structured, and unstructured data as it lands in data lakes, generating context-rich quality metadata consumable by both humans and AI agents. Enterprise data teams use Telmai to automate pipeline validation, detect anomalies, and resolve incidents without manual toil. The platform integrates with 250+ tools including cloud storage, warehouses, and table formats like Apache Iceberg and Delta Lake, and supports natural-language querying for non-technical users. Key features include a suite of Data Reliability Agents—Orchestration, Validation, Incident Diagnosis, Lineage, Data Insight, Help, and Routing—that automate onboarding, rule suggestion, root cause analysis, lineage mapping, and user guidance. The Data Health Dashboard offers real-time health metrics, and the platform provides anomaly detection on batch and streaming data, custom SQL monitors, automatic schema drift detection, and collaborative incident management. Telmai also supports the Model Context Protocol (MCP) for AI agents to query validated data context. Telmai’s native support for open architectures (Iceberg, Delta, Parquet) and its focus on making data AI-ready differentiate it from competitors like Monte Carlo or Sifflet, which are more warehouse-centric or proprietary-format oriented. The platform is trusted by companies like ZoomInfo, DataStax, PropertyGuru, and Clearbit, and has been recognized as a G2 High Performer. Recent developments include the launch of Data Reliability Agents, a partnership with Databricks and Google Cloud, and integration with Microsoft Fabric. Telmai positions itself as an autonomous-ready solution for the agentic AI era, emphasizing reduced alert fatigue and engineering overhead.