The Future Is Unstructured: How Anomalo + Snowflake Cortex Make Unstructured Data Ready for AI

As the Snowflake community gathers this week in San Francisco for the Snowflake Summit, one message is clear: AI is here, and it’s changing how we work with data. But behind every breakthrough model, agent or app is one critical prerequisite: trustworthy data. And today, the most untapped, unruly source of enterprise knowledge is unstructured content.

That’s why we’re excited to announce that Anomalo’s Unstructured Data Monitoring is now available for Snowflake customers. It brings end-to-end monitoring, classification, redaction, and quality validation to unstructured data, directly from your Snowflake environment.

And best of all, customers can run Anomalo’s Unstructured Data Monitoring as a Snowflake Native App, leverage Cortex hosted models, without moving data outside your Snowflake environment. 

Anomalo’s GM of Gen AI Products Vicky Andonova is giving a talk at the Snowflake Summit on the Unstructured Data Monitoring product on Tuesday, June 3 at 3:30 p.m.

Unstructured Data Is the Foundation for AI, But Also the Most Unknown

Snowflake’s Cortex AI platform, Snowpark, and Streamlit make it easier than ever to build generative AI applications. But if your app is powered by low-quality, unfiltered, or non-compliant documents, your models can hallucinate, leak sensitive data, or fail to deliver meaningful business value. From PDFs and contracts to policy docs, screenshots, and web archives, you’ll need a way to monitor it with the same rigor as your structured data assets.

With Anomalo’s Unstructured Data Monitoring product, enterprises can curate unstructured text documents and evaluate them for data quality around various document and document collection characteristics, including document length, duplicates, topics, tone, language, abusive language, PII and sentiment. Customers can quickly assess the quality and fitness of a document collection and identify issues in individual documents, dramatically reducing the time needed to curate, profile and leverage high-value unstructured text data. In addition to Anomalo’s 15 out-of-the-box issues, customers can create their own custom issues to look for and designate what classifies as high or low quality for their documents with custom severity scores.

Unstructured Data Monitoring also lets enterprises extract insights from the vast volumes of unstructured data stored in Snowflake. A key feature, Anomalo Workflows, is a hub for managing and monitoring unstructured data, moving the product beyond just being a platform for data quality.  With Workflows, customers can: 

• Identify and correct quality issues like duplicates, errors, PII and abusive language
• Analyze large volumes of unstructured content to uncover patterns and extract meaningful insights
• Convert unstructured content into structured data ready for downstream analytics and AI workflows
• Curate document collections into clean, reusable sets for training or retrieval

Build AI with Confidence, All in Your Snowflake Stack

Anomalo connects directly to Snowflake tables or a customer’s object storage, no ETL or re-platforming required. It uses LLMs to classify, tag, redact, and analyze content for anomalies and quality issues, so only the most relevant, safe, and complete documents feed your AI apps. Through our integration with Snowflake Cortex—Snowflake’s native suite of AI and ML services—Anomalo can process and classify unstructured assets without moving data outside your Snowflake environment. This means you can rely on models already approved by your organization, with no need to send data to third parties like OpenAI or Anthropic. There’s also no burden of managing infrastructure or configuring open-source models. You get the security and control of on-prem LLMs with the ease of hosted solutions—without compromising on data privacy or performance.

With Anomalo, your unstructured datasets go from “black box” to governed, trusted assets in minutes, not months. Anomalo Unstructured Data Monitoring supports use cases such as:  

1. Document Classification: Anomalo can scan documents in Snowflake and apply ML-based classification to label files (e.g., “legal”, “contract”, “internal policy”) using Cortex hosted models.
2. Semantic Analysis: Using Cortex hosted models, Anomalo performs semantic tagging and metadata extraction, identifying key document attributes like IP addresses, languages, and categories
3. Auto-tagging & Contextual Enrichment: Cortex hosted models allow Anomalo to dynamically auto-label new documents based on learned classifications (e.g., learning what constitutes a “contract” or “agreement” across the dataset).

Available via Snowflake Marketplace and Snowflake Native App 

Just like Anomalo’s core product for monitoring the quality of structured data, Anomalo’s Unstructured Data Monitoring product is also available on the Snowflake Marketplace, and eligible for procurement through Snowflake’s Marketplace Capacity Drawdown program. Unstructured Data Monitoring is also available through Anomalo’s existing Snowflake Native App. Anomalo was the first company to build a data quality solution that is fully containerized within Snowpark Container Services, ensuring that data remains within the customer’s environment. Snowflake customers can find the app listing on Snowflake Marketplace.

If you’re a Snowflake customer planning to scale AI or modernize your data governance, this is the moment to turn your unstructured data into a competitive advantage. Join us at Snowflake Summit this week, or request a personalized demo to see how Anomalo can help your team accelerate enterprise Gen AI readiness.