Deasie connects to any vector database and can generate metadata from both (i) embeddings or (ii) underlying documents
Deasie's model backward engineers the best metadata schema from your corpus of documents
Deasie extracts metadata at both chunk & document level, which is hierarchical, multi-modal and standardized
Deasie's retrieval agent uses the metadata to select the most relevant information and agents
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.
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.
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 new metadata through LLM-powered labelling
Hierarchical metadata
Deasie infers relationships between documents to build hierarchical 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
Export & integrate
Direct connection of metadata back into underlying vector databases
Retrieve
Intelligent selection and filtering of the most relevant pieces of information
Maintain metadata
Continuous & automated maintenance of metadata, including dynamic taxonomies
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 applications.
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
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.