Deasy Labs

Deasy Labs turns unstructured organizational data into AI-ready datasets by enriching, curating, and tagging content so AI systems can retrieve relevant, high-quality information. It focuses on mapping data, removing unwanted content, and enriching with metadata at scale to support RAG, search, and AI agents.

Connect to data sources (e.g., SharePoint, S3), OCR and parse files, chunk content, and normalize unstructured data. Automatically tag thousands of files per minute with metadata, build domain-specific taxonomies, detect sensitive data, and assess quality/relevance. Slice data by relevance, topic, time, quality, or sensitivity to create purpose-built datasets for RAG, search, and agents. Write enriched metadata back to source systems or downstream pipelines, and continuously refresh datasets to prevent staleness. Supports integration with existing models/LLMs and deployment in customers’ own environments with a single metadata standard across teams.

Who it’s for: Medium to large enterprises with substantial unstructured data needs, especially those using SharePoint, email archives, and PDFs, aiming to improve AI data quality, governance, and efficiency of retrieval and RAG pipelines.

customer-facing platform with enterprise features (petabyte scale, on-premise deployment, governance, data enrichment) and mentions of case studies/testimonials; hiring/traction indicators not explicit in text