About Deasy Labs

Deasie provides a metadata tagging solution that auto-generates and standardizes metadata from unstructured data sources, enabling efficient retrieval and management of information. This technology addresses the challenge of ensuring data quality and relevance for generative AI applications by creating hierarchical metadata structures that enhance data governance and usability.

```xml <problem> Enterprises struggle to efficiently manage and retrieve information from unstructured data sources due to the lack of standardized metadata. Ensuring data quality and relevance for generative AI applications is further complicated by the difficulty in matching use cases with the best possible data sets. </problem> <solution> Deasy Labs provides a metadata tagging solution that automates the generation and standardization of metadata from unstructured data, enabling efficient data retrieval and enhanced data governance. The platform connects to vector databases and can generate metadata from embeddings or underlying documents. It backward engineers metadata schemas from document corpora and extracts metadata at both chunk and document levels, creating hierarchical, multi-modal, and standardized metadata. Deasy Labs' retrieval agent uses the generated metadata to select the most relevant information and agents for specific tasks. </solution> <features> - Auto-suggested metadata generation through analysis of large document and image sets - Customizable metadata definition via LLM-powered labeling - Hierarchical metadata inference to build relationships between documents - Automatic standardization and grouping of similar metadata values for easy filtering and updates - Human-in-the-loop validation workflow for testing, analyzing, and refining metadata through reinforcement learning - Quality scores and evidence generation for all metadata to provide easy validation - Direct connection of metadata back into underlying vector databases - Intelligent selection and filtering of relevant information - Continuous and automated metadata maintenance, including dynamic taxonomies - Integration with data sources such as Sharepoint, S3, AzureBlob, and Dropbox - On-prem deployment within private clouds - API access for platform integration - Easy import and export of metadata to connect with existing data systems and MDM tools - User permission management and controls </features> <target_audience> Deasy Labs targets enterprises seeking to improve knowledge management, data governance, and the performance of their AI applications by leveraging high-quality metadata for unstructured data. </target_audience> ```

What does Deasy Labs do?

When was Deasy Labs founded?

Deasy Labs was founded in 2023.

Founded

2023

Employees

8 employees

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Deasy Labs

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Executive Summary

deasie.com 1K+

Founded 2023

Funding

No funding information available.

Team (5+)

No team information available.

Company Description

Problem

Enterprises struggle to efficiently manage and retrieve information from unstructured data sources due to the lack of standardized metadata. Ensuring data quality and relevance for generative AI applications is further complicated by the difficulty in matching use cases with the best possible data sets.

Solution

Deasy Labs provides a metadata tagging solution that automates the generation and standardization of metadata from unstructured data, enabling efficient data retrieval and enhanced data governance. The platform connects to vector databases and can generate metadata from embeddings or underlying documents. It backward engineers metadata schemas from document corpora and extracts metadata at both chunk and document levels, creating hierarchical, multi-modal, and standardized metadata. Deasy Labs' retrieval agent uses the generated metadata to select the most relevant information and agents for specific tasks.

Features

Auto-suggested metadata generation through analysis of large document and image sets

Customizable metadata definition via LLM-powered labeling

Hierarchical metadata inference to build relationships between documents

Automatic standardization and grouping of similar metadata values for easy filtering and updates

Human-in-the-loop validation workflow for testing, analyzing, and refining metadata through reinforcement learning

Quality scores and evidence generation for all metadata to provide easy validation

Direct connection of metadata back into underlying vector databases

Intelligent selection and filtering of relevant information

Continuous and automated metadata maintenance, including dynamic taxonomies

Integration with data sources such as Sharepoint, S3, AzureBlob, and Dropbox

On-prem deployment within private clouds

API access for platform integration

Easy import and export of metadata to connect with existing data systems and MDM tools

User permission management and controls

Target Audience

Deasy Labs targets enterprises seeking to improve knowledge management, data governance, and the performance of their AI applications by leveraging high-quality metadata for unstructured data.