Apply for Anomalo’s Unstructured Data Capabilities in Private Beta
Build trust in your unstructured data powering Gen AI applications.
Apply Now
Enterprises are sitting on a treasure trove of unstructured document data: customer support conversations, user generated content, internal documentation, and regulatory filings to name a few. But this data can be rife with data quality issues. Documents are incomplete, poorly written, or duplicated. Or content contains abusive or inappropriate language, proprietary information, or sensitive personally identifiable information. Enterprises must understand and manage the quality of this data before their Gen AI aspirations will bear fruit.
Anomalo’s new Automated Document Data Quality 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 potential data quality issues in every document (see product images). Each document is scored from 1 (lowest quality) to 10 (highest quality), and scores and issues are aggregated and analyzed across relevant collections of documents.
Anomalo runs entirely within your Virtual Private Cloud (VPC). Anomalo seamlessly integrates with your cloud provider’s Model as a Services (MaaS) platform, such as AWS Bedrock, Google Vertex AI, or Azure AI to leverage state of the art large language models to assess the quality of your documents. None of your data leaves an environment you control, and your data is never used to train or fine-tune models.
Use Anomalo to identify:
| Sensitive PII that is present in your transcribed customer support conversations | Customers asking to be removed from contact lists or seeking escalation | Proprietary information present in a dataset that could leak through a Gen AI application |
| Abusive language in a dataset that could be served to users in a RAG application | Documents that are duplicates and might have inflated impact on models or applications | Documents that are incomplete, contradictory or poorly written and should be removed entirely |
| Documents with structured metadata fields that are inconsistent with the document contents | Customize the Anomalo platform using structured prompts to identify issues that are unique to your business, data, or objectives. |
Get Started
Meet with our expert team and learn how Anomalo can help you achieve high data quality with less effort.