Product Overview

Unstructured Data Monitoring

AI-ready quality and insights for every document in your enterprise

Is Your Enterprise Ready for Gen AI?

Enterprises are sitting on a treasure trove of unstructured document data: from customer support transcripts and user-generated content to internal documentation and regulatory reports. But this data is often messy: incomplete, redundant, poorly written, or laced with quality concerns like abusive language, proprietary details, or sensitive personally identifiable information. Before organizations can fully harness this data for generative AI and other strategic initiatives, they must first ensure its quality, integrity and safety.

Anomalo’s Unstructured Data Monitoring solution helps enterprises measure and manage the quality of their document data stores. Anomalo uses foundational large language models to search for a wide range of common and custom data quality issues in every document. Each document is scored from 1 (lowest quality) to 10 (highest quality), with insights aggregated across entire collections, so teams can prioritize what to fix, filter, or flag.

Give your teams the confidence to build with Gen AI — on data they can trust.

Sensitive PII

Sensitive PII that is present in your transcribed customer support conversations

Customers' removal requests

Customers asking to be removed from contact lists or seeking

Proprietary information

Proprietary information present in a dataset that could leak through a Gen AI application

Documents with structured metadata

Documents with structured metadata fields that are inconsistent with the document contents

Custom structured prompts

Customize the Anomalo platform using structured prompts to identify issues that are unique to your business, data, or objectives.

What You Can Do with Anomalo for Unstructured Data

Creating Customer Support AI

Train and deploy customer support agents confidently, using data you trust

Problem: Training customer support AI on messy, incomplete, or sensitive data is like building on quicksand. Enterprise applications can’t rely on a black box.
Solution: Anomalo ensures your AI starts with clean, reliable unstructured data. Clean inputs = Confident outputs.

1. Detect Issues Immediately

Hook up your unstructured data source (e.g., S3 bucket) to Anomalo. Run 15+ out-of-the-box checks and define custom ones. Spot problems like missing metadata, corrupted documents, unreadable formats, and more.

2. Fix Issues with Automation

Use Anomalo Workflows to create a clean, AI-ready dataset automatically. For example, you can redact PII and remove conflicting documents based on your checkmark criteria.

3. Monitor Continuously

Save your monitoring setup and let Anomalo run checks automatically. Choose your cadence—daily, weekly, or whatever fits your schedule.

Extracting Revenue-Generating Insights

Drive revenue with automated scalable analysis, not manual tagging.

Problem: Teams waste hours manually reviewing and categorizing documents to spot trends. Doing this is slow, inconsistent, and impossible at enterprise scale.
Solution: Anomalo gives you automated, context-aware insights from unstructured data. Real-time trends, customer signals, and opportunities. No manual lift.

1. Choose Your Data Source

Select documents by metadata conditions or use a natural language prompt. Example: “Find reviews and support tickets mentioning a bad experience.”

2. Define the Insights You Want

Tell Anomalo what patterns or insights you’re looking for: from emerging product complaints to churn signals or trending themes across customer feedback.

3. Get Fresh Insights, Automatically

Save your analysis setup and let Anomalo synthesize insights continuously. Choose your cadence and integrate it into your workflows (e.g., dashboards, alerts, end-of-month reports, and summaries).

Our Customers

What Customers Say

“In the restaurant service industry, understanding and acting on guest experiences is critical—and that means unlocking insights from the tens of thousands of unstructured comments we receive each month. Through our collaboration with Anomalo, we’ve started exploring how their Unstructured Data Monitoring can surface meaningful patterns in support tickets and guest feedback. We’re excited about the power to turn this data into actionable insights, strengthen our GenAI initiatives, and bring high-quality unstructured data into everything we build.”

Sid Stephens

Data Governance Lead

Enterprise-Grade Security and Deployment

Anomalo can run entirely within your Virtual Private Cloud (VPC). Our data quality solution integrates seamlessly with your cloud provider’s Model as a Services (MaaS) platform, such as AWS Bedrock, Google Vertex AI, Azure AI or Snowflake Cortex and Databricks to leverage state of the art large language models to assess the quality of your documents.

Whichever deployment you choose, none of your data leaves an environment you control, and your data is never used to train or fine-tune models.

SaaS Deployment

For enhanced security and compliance, the product is accessed via AWS PrivateLink, Azure Private Link, or Google Private Service Connect, enabling private connectivity between virtual private clouds (VPCs) and cloud services without exposing data to the public internet.

In-VPC Deployment

The application can also be deployed to your own virtual private cloud (VPC). The product integrates seamlessly with cloud provider services for document quality assessment. Importantly, in this configuration, data remains within the enterprise’s controlled environment.

Related Resources

Unstructured Data Monitoring in Private Beta Preview

The Future is Unstructured: Introducing Unstructured Data Monitoring for the Gen AI Era

Major Updates to our Unstructured Data Monitoring Product

Ready to Trust Your Data? Let’s Get Started

Meet with our team to see how Anomalo transforms data quality from a challenge into a competitive edge.

Request a Demo