Monte Carlo, the leading data and AI observability platform, unveiled its latest innovation: unstructured data monitoring. This new capability empowers organizations to ensure the quality and trustworthiness of unstructured data—such as documents, chat logs, and images—without writing a single line of SQL.
As enterprises lean deeper into AI transformation, unstructured data has emerged as a major force. IDC reports that 90% of enterprise data is unstructured, yet most companies lack visibility into its reliability. With this release, Monte Carlo becomes the first data and AI observability platform to offer end-to-end monitoring for both structured and unstructured data assets, addressing a critical blind spot in enterprise data stacks.
Bridging the Gap in Data + AI Observability
The rise of generative AI has turned unstructured data into a cornerstone for analytics, business intelligence, and AI product development. Monte Carlo’s new functionality allows users to configure AI-driven quality checks tailored to unstructured fields—enabling them to track the metrics that matter most to their use cases.
Key applications include:
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Detecting missing or incomplete content in images and text
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Alerting on quality degradation in customer service logs via sentiment analysis
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Validating AI-generated content for tone, factual accuracy, and format
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Identifying off-topic or out-of-place data based on custom classifications
These capabilities are now seamlessly integrated into Monte Carlo’s monitoring engine. With native support for leading platforms like Snowflake, Databricks, and BigQuery, teams can deploy monitoring solutions within minutes—ensuring faster time-to-value and broader coverage across their data ecosystems. Crucially, all operations occur within the customer’s environment, ensuring enterprise-grade data security and compliance.
Also Read: Virtana Launches First Full-Stack AI Factory Monitoring
“Enterprises aren’t just building AI—they’re racing to build AI they can trust,” said Lior Gavish, co-founder and CTO of Monte Carlo. “High-quality unstructured data—like customer feedback, support tickets, or internal documentation—isn’t just important; it’s foundational to building powerful, reliable AI. It can be the difference between a model that performs and one that fails. That’s why we designed our monitoring capabilities to proactively detect issues before they impact the business.”
This launch marks a significant milestone in Monte Carlo’s evolution from a pioneer in data observability to becoming the industry’s first end-to-end data and AI observability solution—providing complete visibility across the modern AI data lifecycle.
Enabling AI-Ready Data Through Strategic Integrations
To support AI-native workflows, Monte Carlo also announced expanded platform integrations with Snowflake and Databricks, strengthening observability across their respective AI analytics frameworks: Snowflake Cortex Agent and Databricks AI/BI.
Monte Carlo’s extended partnership with Snowflake, the AI Data Cloud company, now supports Cortex Agents—AI-powered tools that analyze structured and unstructured data to improve decision-making. This ensures observability is built directly into the intelligent agents that drive enterprise insights.
Additionally, Monte Carlo is deepening its collaboration with Databricks to support Databricks AI/BI, an advanced compound AI system embedded within the Databricks platform. This integration offers comprehensive observability across ETL pipelines, lineage tracking, and AI-generated insights throughout the data lifecycle.
“AI applications are only as powerful as the data powering them,” said Shane Murray, Head of AI at Monte Carlo. “By supporting Snowflake Cortex Agents and Databricks AI/BI, Monte Carlo helps data teams ensure their foundational data is reliable and trustworthy enough to support real-time business insights driven by AI.”
