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