Bringing Trust to Salesforce Data, From Source to Agentforce

Salesforce is home to some of the most valuable data in the enterprise—from pipeline forecasts and customer segmentation to Einstein and Agentforce. But when up to 30% of CRM data decays each year, that value comes with risk.

Manual inputs, broken syncs, and silent schema changes introduce costly errors—and by the time they surface in your warehouse, it’s too late. The result? Broken dashboards, wasted ad spend, and lost trust in the systems driving your revenue and AI. In the age of automation, bad data is no longer just a technical problem…it’s a business one.

Introducing: Native Salesforce CRM + Data Cloud Observability

Today, we’re introducing native integrations with Salesforce CRM and Salesforce Data Cloud, making Monte Carlo the first data + AI observability platform to deliver automated, end-to-end monitoring in Salesforce.

For data quality management to be effective, it needs to be proactive and scalable from end-to-end. However, without the right tools, most teams still rely on manual checks, scripts, or post-hoc alerts — leaving critical issues undiscovered until they surface in downstream systems. Monte Carlo closes that gap by bringing data + AI observability directly in Salesforce, so teams can catch and resolve issues before they ever leave the platform.

Whether you’re managing sales performance, personalizing customer journeys, or deploying LLM agents through Agentforce, Monte Carlo empowers data and AI teams to:

• Monitor data quality at the source, not hours later in the warehouse
• Deliver trusted Customer 360 profiles for accurate, AI-ready insights
• Bridge data, revenue operations, and marketing workflows with a single view into pipeline health and data quality
• Reduce fire drills with automated monitoring, and accelerate root cause analysis

Now, every system and stakeholder downstream of Salesforce can operate with confidence.

From Broken Pipelines to Business Confidence

With Monte Carlo’s integration, users can set up automated monitoring in just a few clicks directly inside Salesforce CRM and Data Cloud. With this release, teams can:

• Catch issues at the source, before they impact downstream dashboards, forecasts, or campaigns using custom monitors
• Ensure consistency across systems, with side-by-side comparisons of Salesforce and your warehouse or lakehouse
• Prevent silent failures, thanks to schema change detection inside Salesforce
• Deliver AI-ready data, so Einstein, Agentforce, and AI initiatives work with confidence

Users are already identifying business-critical use cases for this new integration:

“Monte Carlo’s Salesforce integration will unlock a whole new level of confidence in our CRM data. We’re excited to catch issues—like missing fields or broken syncs—before they hit Snowflake or affect reporting. 

Moreover, as we are in a process of deprecating formula fields, we will use this feature to ensure reliability across all pipelines. We’ll be able to stop bad data before it affects our Go-To-Market operations, improving executive trust in our insights. 

Best of all, it will save our OPs teams hours and promoting data quality as everyone’s responsibility.”
— Pedro Sá Martins, Head of Engineering, OutSystems

Building the Future of AI-Ready Salesforce Data

When it comes to delivering reliable, trusted data in Salesforce, we’re just getting started. As our customers deepen their investment in Einstein, Agentforce, and AI-powered decision-making, we’ll continue to invest in our Salesforce integrations to support even more use cases.

The goal? To make every Salesforce-powered insight, workflow, and model as trustworthy as the teams behind it. As Salesforce continues to evolve, so will Monte Carlo—ensuring you can trust the data fueling your revenue engine and AI strategy.

See It in Action

Want to see how Monte Carlo’s Salesforce integrations work in the wild? Sign up for our live demo to learn how to detect and resolve Salesforce data issues before they impact your business.

Our promise: we will show you the product.