Anomalo + Atlan Expand Integration to Unstructured Data with App Framework

Last year, Anomalo and Atlan launched a first-of-its-kind native real-time integration to help data teams build trust in their structured data at scale. By combining Anomalo’s AI-first data quality platform with Atlan’s AI-native metadata and governance platform, we gave enterprises a seamless way to detect, resolve, and govern data issues directly within the tools their teams already use. 

That partnership has already driven adoption among some of the world’s most data-driven organizations like a Fortune 500 manufacturer, a global quick-service restaurant chain, and a major digital bank–helping them safeguard mission critical analytics and accelerate data-driven decisions. 

Why Unstructured Can’t Wait

Today, more than 80% of enterprise data lives in unstructured formats—PDFs, contracts, call transcripts, logs, videos, and more. This data fuels generative AI, advanced search, and analytics, but it’s often riddled with hidden risks like outdated or contradictory content, personally identifiable information (PII), inappropriate language, or duplicates that can slip into production.

Unchecked, these issues don’t just slow AI projects—they can lead to model hallucinations, regulatory exposure, and costly reputational damage. As enterprises race to deploy GenAI and agentic applications, the speed and safety of those projects will hinge on whether unstructured data is trustworthy from day one.

These risks don’t stop at the data itself, they ripple into how teams collaborate around unstructured data. Producers struggle to know what context is safe to include, governance teams lack visibility into how sensitive or outdated content might flow into AI models and applications, and consumers risk building on information they can’t fully trust. In the AI era, enterprises need confidence in the quality of unstructured data, along with shared context and governance to ensure it’s being used responsibly across analytics, AI, and business workflows.

Anomalo Unstructured: First-of-its-Kind AI-Driven Monitoring

Earlier this year, we launched Anomalo Unstructured, the industry’s first comprehensive unstructured data quality monitoring solution. Purpose-built for documents, transcripts, support tickets, and beyond, Anomalo Unstructured acts as an AI-driven intelligence layer— doing everything from identifying and correcting quality issues such as PII to generating rich metadata for downstream applications and extracting insights from your verified data. 

By turning messy, unstructured collections into AI-ready datasets—complete with quality scores, content summaries, and governance tags—enterprises can confidently feed them into AI, BI, and operational workflows. One global insurance provider improved AI customer support resolution rates simply by auto identifying and removing PII and outdated and contradictory content from the context fed to its AI RAG system.

Now in Atlan: Trustworthy Context Across All Data Types

As part of Atlan’s new App Framework, Anomalo Unstructured is now integrated directly into Atlan’s Metadata Lakehouse. For the first time, enterprises can unify monitoring for both structured and unstructured data within the same foundation that powers column-level lineage, business-ready data products, AI governance, and policy & compliance monitoring.

With this integration, Atlan customers can:

• Unify context in the Metadata Lakehouse – Anomalo automatically enriches Atlan’s Metadata Lakehouse with unstructured data collections, ensuring complete visibility across all data types with consistent governance.
• Collaborate on unified context – Quality scores, summaries, detected issues, and sensitivity flags feed directly into Atlan, where they strengthen data products, AI governance, and semantic context — enabling teams to align on what’s trustworthy and AI-ready.
• Enforce policy and compliance at scale – Anomalo automatically brings tags into Atlan, which can then be synced back to systems like Databricks and Snowflake through bi-directional tag management or propagated downstream via column-level lineage. These signals can also trigger active metadata playbooks, powering automated governance and compliance workflows.

Why This Partnership Matters

In Atlan’s Head of Global Alliances, Marc Seifert’s words:

“AI governance is quickly becoming one of the biggest challenges for enterprises. Shadow AI projects are proliferating, and without strong data governance, it’s impossible to know whether the data feeding models is safe or trustworthy. By bringing Anomalo’s structured and unstructured data intelligence into Atlan’s Metadata Lakehouse through our App Framework, we’re giving enterprises the foundation they need to govern AI with confidence.”

Amy Reams, Anomalo’s Vice President of Business Development and Marketing, adds:

“Unstructured data is the foundation of the next wave of agentic AI applications within the enterprise — yet most enterprises are not able to effectively use any of their unstructured data. With this new integration, Anomalo becomes the only solution that gives organizations complete, automated visibility into both structured and unstructured data, directly inside Atlan. It’s how you build data trust for the AI era.”

Get AI-Ready—Now

Data quality issues in unstructured assets aren’t just a governance problem—they’re an AI readiness problem. With Anomalo + Atlan, you can detect and fix these issues before they derail your projects, delay launches, or trigger compliance risks.

Explore the integration and book a demo to see how Anomalo can help you get AI-ready—without compromising on data quality.