Anomalo recently announced a $10 million extension of its Series B funding, bringing the round up to $82 million. The company differentiates itself by analyzing raw unstructured data collections before any pipeline has been set up to ingest such data. This allows for broader exploration of all available data before committing to building a pipeline. Anomalo's data quality monitoring can also integrate with the data pipelines that feed into retrieval augmented generation (RAG), ensuring the quality of the information used to generate outputs.
Key takeaways:
- Anomalo, a data quality platform, has expanded its services to support unstructured data quality monitoring, aiming to improve the utility of data for enterprise AI.
- The company's CEO, Elliot Shmukler, believes that their technology can accelerate gen AI deployments by at least 30% by eliminating data quality issues.
- Anomalo has raised an additional $10 million in its Series B funding round, bringing the total to $82 million.
- The platform offers features such as custom issue definition, support for private cloud models, metadata tagging, and an upcoming redaction feature to provide redacted versions of documents.