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Automated labeling of unstructured data

Deasie provides an automated labeling workflow to rapidly label, catalog and filter unstructured data better than any human.

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How leading enterprises use Deasie’s  labeling workflow:

Enhance RAG accuracy & scalability

Scale RAG to 10,000+ documents without losing accuracy by auto-generating hierarchical metadata labels to enhance LLM retrieval.

Automate cataloging of unstructured data

Auto-tag and catalog large volumes of unstructured data for enterprise knowledge management and compliance.

Label & remove sensitive or low quality data before AI

Filter data to ensure no sensitive, duplicate or outdated information is ever used within AI applications.

Enhanced feature engineering for ML

Auto-suggest & extract metadata from large volumes of unstructured data to drive better ML and analytics.

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 labels

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

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

Hierarchical labels
Deasie infers relationships between labels to build hierarchical metadata

Improve labels

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

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

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

Utilize labels

Search data
Rapid search through catalogs of unstructured information

Curate data
Continuous filtering of data to ensure models only use non-sensitive, relevant & up-to-date information

Export & integrate
Direct connection of Deasie’s labels to vector databases, MDM and data storage tools

Hierarchical labeling

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.

Download Report