Wipro HOLMES® Data Labeling Studio

Wipro HOLMES® Data Labeling Studio

With superior project KPI visibility for defined roles and customized dashboards for intuitive, persona-based access, the Wipro HOLMES® Data Labeling Studio equips users with role-based insights into labeling KPIs (Org Admin, Chief Curator, and Curator). NER-based active learning with pre-defined attribute rules makes on-premise deployment and source change tracking possible. The studio also supports varying data types like document, PDF, Word, and images (emails, text, scanned doc), across multiple personas and industry types such as retail, banking and insurance, tech, manufacturing/auto, healthcare, and pharma. Data Labeling Studio is powered by Intel® Xeon® Processors.

*Please note that member solutions are often customizable to meet the needs of individual enterprise end users.



  • Data Security and Lineage
    HTTPS and SSL encryption standards for data in motion, AES 128/ 256 bit for data at rest.
  • Rich, simple, and powerful UI
    Toggle and auto-save features provide access to image and text labelling from a single screen.
  • Auto Labeling
    Automated labeling based on pre-defined attribute rules and Named-Entity Recognition based Active Learning
  • Quality Mechanism
    Inbuilt annotator rating allowing labelers and reviewers to refer the original file on a single screen. Also, built-in document rejection workflow to determine the cause of quality issues, ensuring superior output.
  • Easy Third-Party Integrations and Designed to Scale
    Easy enterprise integration with built-in connectors for SharePoint, SNOW, and File Storages such as AWS S3, FTP, and SFTP. Also, integrated OCR and data transformation toolsets to configure data pipelines based on need.
  • Optimized Labeling Process
    Three-way assignment options and configurable workflows to manage workload seamlessly.


WorldwideIntel® Core™ Processor FamilyIntel® Xeon ScalableCSP - Amazon Web ServicesCSP - Google CloudCSP - Microsoft AzureOn-premise (Private Cloud, Other)KerasTensorFlowCross-IndustryEnergy and UtilitiesFinance and InsuranceModels can be trained - data input only requiredModels can be trained - online learningModels can be trained - requires labeled dataModels cant be re-trained - Inference onlyAI Platform as a Service (AI PaaS)Linux Intel® Distribution for PythonOtherBERTContent generationData Preparations and ManagementMachine Learning