Data governance built for the AI and agentic era

AI initiatives stall when data quality is unknown, ownership is unclear, and reliability is untracked. Monte Carlo gives your teams programmatic visibility into data health — so you can measure AI-readiness, reduce downtime by 80%, and ship with confidence.

Know the health of every data product, across every team

Surface reliability metrics, SLA status, and ownership in one place — so data products get used, not questioned.

Turn data quality from a blocker into a competitive advantage

Bad data kills adoption. Monte Carlo helps data teams produce assets that are reliable, discoverable, and trusted — so the business builds on them, not around them.

Give your AI a foundation it can rely on

AI models are only as good as the data feeding them. Proactively measure and enforce quality standards so your AI teams can build and deploy faster, without second-guessing their inputs.

Critical features

Increase awareness to drive accountability across teams

Reduce downtime by 80% and get your data AI-ready with observability features that maximize engineering resources.

Data product dashboard

Organize health metrics by product to help consumers understand—and trust—their critical data + AI products.

Catalog integrations

Surface data health metrics right in your catalog to make insights easily discoverable.

Data operations dashboard

Easily track the progress of critical data quality initiatives, including incident metrics like frequency, severity, and resolution speed over time.

Data quality dashboard

Surface data quality scores to measure how data fits specific use case requirements.

Full coverage from day one — no months-long setup

Connect Monte Carlo in minutes and get immediate visibility into your most critical data assets. Coverage scales automatically as your environment grows, so you're never playing catch-up with your data.

Cut incident resolution time from hours to minutes

The average data incident takes ~19 hours to resolve. Monte Carlo surfaces root cause and recommended next steps automatically — so your team spends less time debugging and more time delivering reliable data to the business.

Built for every data team, not just the most technical ones

Monte Carlo's agent automation, MCP or intuitive UI mean your entire data + AI team can contribute to quality — from senior data engineers to analytics leads. Implement your governance strategy and get it working in the real world, fast.

Customer Stories

Trusted by enterprises deploying AI at scale

Our customers scale trust, reduce risk, and deliver better business outcomes. See how you can too.

How JetBlue Improved Internal “Data NPS” By 16 Points YoY

The Challenge:

When a data migration improved data usage, increased access brought increased scrutiny. And the trustworthiness of the data took center stage.

The Solution:

Operationalizing data + AI observability and leveraging Monte Carlo's in-app features to measure the outcomes.

Nasdaq's Journey to Reliability with Monte Carlo

The Challenge:

Nasdaq generates 6,000 reports per day across 35 services and 2,200 users. The question is—how do you make that much data reliable?

The Solution:

The team deployed Monte Carlo to monitor its entire data lake via a multi-step deployment.

Get started fast—scale faster.

Fast setup—even faster time to value. Connect to Monte Carlo in seconds, start monitoring out of the box and automatically scale with your environment.