Monte Carlo announced the launch of unstructured data monitoring, a new capability that enables organizations to ensure trust in their unstructured data assets across documents, chat logs, images, and more, all without needing to write a single line of SQL.
With its latest release, Monte Carlo becomes the first data + AI observability platform to close this gap, providing AI-powered support for monitoring both structured and unstructured data types.
Monte Carlo goes beyond the standard quality metrics and allows customers to use custom prompts and classifications so as to make monitoring truly meaningful.
Example use cases include:
- Flagging texts or images that miss critical details
- Alerting on drifts in quality of customer service transcripts, as measured by customer sentiment
- Validating model-generated outputs for tone, structure, or factual grounding
- Surfacing content that doesn’t belong based on topic classification
Now, the ability to monitor these and other unstructured data types is fully integrated into Monte Carlo’s monitoring engine and can be deployed with just a few clicks.
Supported warehouse and lakehouse technologies include Snowflake, Databricks, and BigQuery, with native integration into each platform’s respective LLM or AI function libraries, so that sensitive data never leaves customer environments. Teams can create and deploy monitors with minimal setup, ensuring faster time-to-insight and broader coverage.
“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.”
Monte Carlo’s expansion into monitoring unstructured data is part of our broader vision to provide visibility across the data + AI lifecycle, the company’s strategic evolution from a standalone data observability pioneer to the industry’s first end-to-end data + AI observability solution.
Monte Carlo is also announcing integrations with both Snowflake and Databricks to support observability for their respective AI-native analytics platforms: Snowflake Cortex Agent and Databricks AI/BI.
Monte Carlo continues its strategic partnership with Snowflake, the AI Data Cloud company, to support Snowflake Cortex Agents, Snowflake's AI-powered agents that orchestrate across structured and unstructured data to provide more reliable AI-driven decisions.
In addition, Monte Carlo is extending its partnership with Databricks to include observability for Databricks AI/BI – a compound AI system built into Databricks’ platform that generates rich insights from across the data + AI lifecycle – including ETL pipelines, lineage, and other queries.
“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.”
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