Organizations struggle to effectively leverage unstructured data, which constitutes the vast majority of enterprise information, for AI and analytics initiatives. This data, including documents, transcripts, and emails, remains largely unmanaged and inaccessible, hindering the development of accurate AI models and efficient data-driven workflows.

Deasy Labs provides an automated metadata workflow platform designed to transform unstructured content into structured, enriched data assets. The platform automatically discovers and derives schemas and taxonomies from raw data, enabling efficient tagging, filtering, and enrichment of files. This process creates a robust data foundation, facilitating the curation of specific data slices and seamless export of metadata for downstream AI applications, such as generative AI and semantic search.

Automated schema and taxonomy generation from unstructured content without requiring domain expert input.

LLM-based tagging capabilities to extract and synthesize file-level metadata from document chunks.

Human-in-the-loop validation and fine-tuning studio for metadata accuracy.

Data slicing functionality to filter and curate relevant subsets of unstructured data for specific use cases.

Direct export of metadata to cloud storage systems and vector databases for downstream consumption.

API access for programmatic integration into existing AI and data workflows.

Support for connecting to raw files and vector databases.

Automated maintenance and updates of metadata taxonomies as data evolves.

Deployment options within private cloud environments for enhanced security.

Data and AI teams within enterprises seeking to build a structured data foundation from unstructured content for AI, cataloging, and compliance use cases.