Trusted analytics starts with data and AI observability

An insight is only as good as the data behind it. Monte Carlo gives analysts the data quality for analytics they need to work with confidence — automated monitoring, instant alerts when something breaks, and visibility into what you can trust before you present it.

Know what you can trust

See the health of your data at a glance and get automatically alerted when something in your domain looks wrong, before a stakeholder finds it first.

Make your insights count

When your data is reliable, your recommendations get acted on. Improve the trustworthiness of your outputs and increase your impact on the decisions that matter.

Protect the quality of data you share externally

When data is your product, quality is your reputation. Maintain reliability standards for data monetized and delivered to external parties — and know immediately when it falls short.

Built for data analysts

Monte Carlo gives you visibility into what's healthy, what's broken, and what's been fixed — so you can share insights with confidence and stop chasing engineers for answers.

Understand the health of your data + AI products

See the health of your data — without needing to ask. Review dashboard-level health visualizations across your critical assets to spot trends, track reliability over time, and understand what's in good shape before you use it.

Know when breaks happen—and what was impacted

Get notified the moment something breaks — with full context. Automated alert routing keeps you and your domain team informed about new incidents, what's affected, and what's already being done to fix it — no Slack pings to engineers required.

Take control of your data + AI with observability agents

Set up monitoring yourself — no SQL, no waiting. Use AI-powered recommendations to quickly understand what's in your critical tables and deploy business rules from scratch, without filing a ticket or knowing a line of code.

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.