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Metadata orchestration for RAG

Deasie provides the best way to create and leverage metadata within your Retrieval Augmented Generation (RAG) pipelines

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How leading enterprises use Deasie’s  metadata workflow:

Direct connection to vector embeddings

Deasie connects to any vector database and can generate metadata from both (i) embeddings or (ii) underlying documents

Auto-suggested metadata from your data

Deasie's model backward engineers the best metadata schema from your corpus of documents

Generate best-in-class multi-modal metadata at scale

Deasie extracts metadata at both chunk & document level, which is hierarchical, multi-modal and standardized

Use metadata for node selection with our retrieval agent

Deasie's retrieval agent uses the metadata to select the most relevant nodes for a given query

Trusted by Leading Enterprises

Deasie's metadata tagging solution for unstructured data has profoundly transformed our enterprise's knowledge management landscape. Their speed has allowed us to test the viability of our in-house AI product far quicker than expected, with their data preparation capability playing a critical step in the product workflow.

Sam Grice
CEO of Octopus Legacy (Financial Group)

Deasie's industry leading data governance and labeling capabilities have been invaluable in allowing us to achieve transformational automation and top line growth with speed to value, accuracy and reliability. The Deasie team has been top notch in their technical and problem solving capabilities to enable this.

Beth Pollack
Applied AI and Data Strategy Operating Partner, Decision Science Advisors

"What didn’t exist was a good approach for measuring data quality and relevance for unstructured data … Nobody was directly solving the issue of matching every generative AI use case with the ‘best’ possible set of data. Deasie has developed novel approaches in this domain."

How it Works

Define metadata

Auto-suggested metadata
Deasie analyses large document & image sets to auto-suggest the most relevant metadata for your use case

Customisable metadata
Deasie enables anyone to easily define any new metadata through LLM-powered labelling

Hierarchical metadata
Deasie infers relationships between documents to build hierarchical metadata

Improve metadata

Auto-standardised
Automatic standardisation and grouping of similar metadata values to enable easy filtering and updates

Intelligently validated
Human-in-the-loop validation workflow to test, analyse and refine metadata through reinforcement learning

Traceable
Quality scores & evidence generated for all metadata to provide easy validation

Utilize metadata

Export & integrate
‍Direct connection of metadata back into underlying vector databases

Retrieve top nodes
Intelligent selection and filtering of the most relevant nodes for a given user query in the RAG pipeline

Maintain metadata
‍Continuous & automated maintenance of metadata, including dynamic taxonomies

Hierarchical metadata

Deasie’s auto-detects relationships between metadata labels in order to build a hierarchical representation of the unstructured dataset. Hierarchical labels enable more efficient labelling, insight generation, and can be exported to enhance retrieval sequencing in AI retrieval (RAG) applications.

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Built for seamless
enterprise deployment

Integrate with any data source (e.g., Sharepoint, S3, AzureBlob, Teams, Dropbox, … )

Deploy Deasie on-prem within your private cloud

Use Deasie as a platform or API''

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

Auto-label tens of thousands of documents in rapid succession

Manage user permissions and controls

Download our report on the role of metadata in GenAI

Most companies are yet to realise the importance that high-quality metadata labels will play in the upcoming era of AI deployment. Download our report to get an in-depth analysis on the impact that metadata can have on building safe, scalable and accurate LLM applications.

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