Anomalo

Intelligent alert routing and false positive suppression that sends the right alerts to the right people with minimal noise.

Intelligent alert system with false positive suppression, rich alert experience, and sophisticated routing to ensure notifications reach the right team members without noise.

A comprehensive autonomous data system comprising nine specialized AI agents that autonomously monitor, investigate, surface, and report on data issues and insights without requiring code, prompts, or manual intervention.

A conversational analytics agent that enables natural language queries, data analysis, and report composition on enterprise data. Learns from user interactions to continuously improve system knowledge.

Automatically identifies missing and anomalous data patterns registered in the platform using machine learning algorithms.

Tracks upstream and downstream data lineage to help understand data dependencies and impact analysis during issue investigation.

AI-powered platform that uses unsupervised machine learning to automatically detect data quality issues, anomalies, and missing data across tables at scale.

Provides instant root cause analysis when data quality issues are detected, with samples, visualizations, and upstream/downstream lineage to accelerate issue resolution.

Continuously monitors business metrics defined in natural language and alerts users to unexpected changes or anomalies without requiring manual dashboard checks.

Integrations with data catalogs to provide unified data governance and quality monitoring across data discovery and metadata management platforms.

Anomalo's Intelligent Data Analyst that allows users to ask questions, perform data analyses, and compose data-driven reports entirely through natural language, with deep understanding of data structure and usage.

Builds continuously-updated dashboards and reports via natural language, leveraging templates and including built-in time-series predictive modeling for anomaly detection.

Automatically creates comprehensive, up-to-date documentation for datasets by synthesizing catalog metadata, existing documentation, and relevant conversations.

Proactively and autonomously identifies noteworthy changes in data and delivers analyst-grade reports describing what happened, without requiring prompts or analyst involvement.

Automatically investigates data issue alerts, assesses impact and criticality, and takes action by executing established runbooks, creating tickets in tools like ServiceNow/JIRA, and escalating to humans when needed.

Automated upstream and downstream data lineage tracking to understand data dependencies and impact across the data stack.

Monitors data quality by learning what good data looks like through natural language descriptions and automatically detects deviations that could break dashboards, reports, and AI models.

Unsupervised machine learning-powered data quality checks that automatically identify data issues without requiring manual rule configuration.

Automated data quality monitoring platform using unsupervised machine learning to detect missing, anomalous, and problematic data across tables in real-time.

No-code UI for creating custom data validation rules and key metric checks without requiring technical expertise or API configuration.

Cost-effective monitoring capability designed to handle millions of tables with bulk configuration, efficient hourly queries, and orchestration integrations.

Automatically evaluates experiments and A/B tests by applying appropriate statistical techniques, drawing conclusions, and analyzing results across customer segments based on natural language setup.

Sends the right alerts to the right people with false positive suppression and sophisticated routing logic to minimize alert noise.

User-friendly interface enabling business users to create custom data validation rules and key metric checks without coding.

Integration capabilities with data orchestration platforms to enable seamless monitoring within existing data pipeline workflows.

Automatic root cause analysis that pinpoints the likely source of data quality problems, with samples and visualizations for deeper investigation.

Provides always-on monitoring of data availability, freshness, and schema consistency to ensure data pipelines are functioning as expected.

Integrates with ticketing systems to enable automated triage and resolution workflows for data quality issues.