Just Launched: Unstructured Data Monitoring

Bad data has always eroded stakeholder trust; what’s new today is the type of bad data that’s eroding it. 

Internal documents, support tickets, product descriptions and images, chat logs… all once siloed and ignored are now fueling the development of AI applications. But as AI adoption accelerates, unstructured data like text and images isn’t just becoming more critical—it’s also becoming more opaque.

According to IDC, 90% of enterprise data is unstructured, but most organizations still lack the appropriate visibility into its quality. Without observability into both structured and unstructured data, AI development will only serve to amplify performance and regulatory risks—and undermine the value of data + AI teams in the process. 

That’s why Monte Carlo just launched unstructured data monitoring, to bring end-to-end observability to the unstructured data powering all those critical data + AI pipelines.

Read on to find out what this new feature does, how it works, and why it’s the future of unstructured data quality. 

What is Unstructured Data Monitoring?

Today, we’re introducing Monte Carlo’s unstructured data monitoring: the first no-code, AI-powered monitoring tool designed to bring reliability to your unstructured data.

With this launch, data and AI teams can apply intelligent monitors to text and image fields, including reviews, support tickets, descriptions, chat logs, and more, all directly within Monte Carlo’s no-code monitor builder.

Fully integrated with existing workflows — including anomaly detection, alerts to Slack/Teams/others, and incident management — unstructured data monitoring extends the same trusted observability to the unstructured text and images powering your AI models and decisions.

Built for scale, with cost guardrails baked in, this capability ensures the quality of not just structured data but the messy, real-world unstructured data behind AI and analytics — all without writing a single line of code.

Trust Your Data, Trust Your AI

As adoption of LLMs, agents, and AI-powered products accelerates, teams are generating and consuming more unstructured data than ever. And yet, customers consistently tell us that observability for these AI pipelines, and the data flowing through them, is a major blind spot.

Monte Carlo’s unstructured data monitoring brings that blind spot into focus to unlock the value of unstructured data formats for real world use cases. 

• Spot spikes in negative sentiment in customer reviews or support conversations before they escalate
• Catch tone drift in AI-generated responses, chatbots, or customer communications.
• Ensure LLM and agent performance stays consistent as user inputs, prompts, and models evolve
• Monitor RAG pipelines by ensuring alignment between retrieved context, user queries and generated answers
• Track shifts in topics covered in support tickets, call transcripts, or user feedback
• Validate whether product descriptions, tags, or metadata align with actual product content or images

Built for Everyone on the Data + AI Team

Unstructured data monitoring isn’t just a tool for one role—it’s a foundation for trust across the entire data + AI ecosystem. Whether text is flowing through pipelines, powering models, driving dashboards or shaping customer experiences, everyone depends on it being accurate, compliant, and reliable.

This capability brings observability to the heart of data + AI operations. It flags sensitive or non-compliant content before it becomes a risk. It tracks shifts in the quality of model inputs and outputs, helping teams catch drift, spot anomalies, and debug prompt failures. It removes the guesswork from understanding how text data flows and transforms—from ingestion all the way to production AI experiences.

Before you can trust your AI, you have to trust the data that’s powering it. Data + AI observability with unstructured data monitoring is helping organizations across industries to unlock the value of AI by empowering everyone on the data + AI team to deliver trustworthy unstructured data at scale. 

What’s Next

As AI use cases expand to production, trust in unstructured data is non-negotiable. Data + AI observability is the only end-to-end solution that can empower teams to effectively detect, manage, and resolve incidents at scale—in the unstructured data and beyond. 

This launch is just the beginning. We’re working closely with customers to expand support with new integrations, richer out-of-the-box use cases, and even deeper visibility into the data + AI ecosystem.

Curious how Monte Carlo can make your AI pipelines more reliable? Request a demo below.

Our promise: we will show you the product.