Monte Carlo Data

Founded by Barr Moses in 2019

Monitor data pipelines and autonomous AI agents to catch errors before users do

Pricing model

Paid

Rating

★ Positive

Time to value

Immediate

You can use Monte Carlo to track data reliability and monitor autonomous AI agents across your entire organization. It watches your pipelines for missing records, unexpected schema changes, and abnormal values, alerting engineering teams before bad data affects critical reports or applications. You can trace issues from raw database tables all the way to downstream business dashboards to find the root cause of downtime. It also monitors model predictions and agent actions to ensure company systems stay dependable and accurate across complex infrastructure.

What Monte Carlo Data does

Set up automated alert rules for anomalous data volume spikes or dropsTrace field-level data lineage across warehouse tables and downstream reportsInvestigate schema modifications causing pipeline jobs to failMonitor data freshness to ensure batch transformations finish on scheduleAssign and triage data incident tickets to engineering ownersTrack AI agent prediction drift and underlying data quality issuesEnd-to-end data pipeline lineage mapping from tables to dashboardsAutomated anomaly detection across volume, freshness, and schema changesRoot cause analysis to quickly identify where pipeline breakages occurObservability tracking for production AI agents and machine learning outputsIntegrations with common cloud warehouses, lakes, and business intelligence toolsAutomated incident alerts routed directly to engineering notification channels