Anomalo Advances Unstructured Data Monitoring Product With New Breakthrough Workflows, Bringing Value and Trust to the Trove of Unstructured Data Used for Gen AI 

/EIN News/ -- PALO ALTO, Calif., May 29, 2025 (GLOBE NEWSWIRE) -- Anomalo, the company reinventing enterprise data quality, today announced that it has added a major innovation, Workflows, to its groundbreaking second product, Unstructured Data Monitoring, and made the entire product generally available. Unstructured Data Monitoring lets enterprises extract insights and identify issues from the vast volumes of unstructured data stored in their data warehouses, data lake and cloud storage locations. Anomalo Workflows is a hub for managing and monitoring unstructured data, moving the product beyond just being a platform for data quality.

Anomalo was the first to announce AI-powered monitoring of unstructured text in June of last year and added additional breakthrough capabilities in November. Anomalo’s Unstructured Data Monitoring with Workflows furthers the company’s mission to provide data trust across all data types and all data use cases. 

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. Nationwide is giving a talk at Databricks Data + AI Summit the following week on Thursday, June 12 at 12:20 p.m. that will cover enterprise data governance and preparing the enterprise for Gen AI initiatives. 

“Everyone’s talking about unstructured data for Gen AI but the real breakthrough is solving for both quality and insights within this type of data. You can think of our Unstructured Monitoring product and Anomalo Workflows as building blocks that can be assembled in thousands of configurations to achieve pretty much any customer use case for unstructured data quality or insights. Take one of our large retail customers who is trying to mine support tickets and call logs to understand why customers are unhappy. That kind of analysis wasn’t easily possible before Anomalo. Just as we redefined data quality for structured data, we’re now helping enterprises trust and extract value from unstructured data at a scale no other tool can match,” said Elliot Shmukler, co-founder and CEO of Anomalo. 

Anomalo’s first product uses AI to automatically detect issues in structured data, letting teams resolve any hiccups with their data before making decisions, running operations or powering AI and machine learning workflows. The product is used by customers in every major vertical, six of the Fortune 50 and four of the world's six largest telecom companies.

However structured data only represents 20% of the picture. The other 80% of data - documents, call transcripts, emails, messages and order forms - is unstructured, and it is quickly becoming critical to enterprise success as organizations look to power Gen AI workflows. Whether deploying RAG systems or customer-facing chatbots, enterprises need to bring high-quality, domain-specific data to their LLMs. The challenge lies in the unknown. Companies do not know what is in their unstructured data, let alone trust it, making it hard to bring production-ready Gen AI applications to market. 

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.

Anomalo Workflows now lets customers:

• 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 Gen AI workflows
• Curate document collections into clean, reusable sets for training or retrieval

Customers can tailor the platform to their specific use case and get results fast. For example, Anomalo can analyze more than 100,000 documents in one run and can run continuously as new data comes in so what previously was a manual never ending task that could take months, now takes 10 minutes with Anomalo. 

“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 Gen AI initiatives and bring high-quality unstructured data into everything we build,” said Sid Stephens, data governance lead at one of the largest fast food chains in the US.

About Anomalo
Anomalo is reinventing enterprise data quality with an AI-powered data quality platform. Anomalo uses machine learning to replace traditional rules-based systems and automatically detect and alert teams about data quality issues across both structured and unstructured data. With seamless integrations across the entire data stack, Anomalo ensures customers can confidently operate with data and AI before data quality impacts downstream business decisions, customer-facing applications and machine learning models. Anomalo is backed by Databricks Ventures, Snowflake Ventures, SignalFire, Smith Point Capital, Norwest Venture Partners, Foundation Capital, Two Sigma Ventures, Village Global and First Round Capital. For more information, visit https://www.anomalo.com/.

Media and Analyst Contact:
Amber Rowland
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