Telmai

Founded by Mona Rakibe in 2020

Monitor data pipelines and detect anomalies across data lakes and warehouses

Pricing model

Demo

Rating

★ People love it

Time to value

Quick Setup (< 1 hour)

You can use Telmai to monitor data quality and track anomalies across your data pipelines, data lakes, and lakehouses. It tests incoming structured, semi-structured, and unstructured data continuously to ensure accuracy before downstream applications or AI models consume it. Telmai offers automated agents that handle pipeline orchestration, validate datasets, diagnose incident root causes, and trace data lineage. Both technical engineers and business teams can query data quality metrics using natural language, set custom validation rules, and receive plain-text explanations whenever anomalies occur.

What Telmai does

Inspect incoming datasets for missing values or unexpected formatsGenerate data lineage graphs to map pipeline dependenciesSet automated data validation rules for data lake tablesInvestigate anomaly alerts with plain-text diagnostic summariesAudit write-audit-publish operations in lakehouse environmentsExport data quality metadata to data catalogs and external workflowsMonitors structured, semi-structured, and unstructured data as it enters data lakesAutomatically learns data patterns to suggest validation rulesTraces data lineage across sources and targets to show downstream impactsDiagnoses root causes of pipeline incidents and anomaliesTranslates data quality metrics and issues into plain EnglishEnables AI agents to query data health metadata via Model Context Protocol

Pricing breakdown

Pricing model: Book a demo

Frequently asked

BigQuery

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