Retail — Named Entity Extraction for SOPs and Playbooks
FreeThis DAG automates the extraction of named entities from SOP and Playbook documents, enhancing data accuracy and accessibility. It leverages natural language processing to streamline document analysis in the retail sector.
Overview
The primary purpose of this DAG is to extract named entities from Standard Operating Procedures (SOPs) and Playbooks, utilizing advanced natural language processing techniques. The data sources for this workflow include DOCX and PDF files that contain operational guidelines and procedures. The ingestion pipeline begins with the collection of these documents, followed by a series of processing steps that include extraction, normalization, and validation of entities. During the extraction phase, t
The primary purpose of this DAG is to extract named entities from Standard Operating Procedures (SOPs) and Playbooks, utilizing advanced natural language processing techniques. The data sources for this workflow include DOCX and PDF files that contain operational guidelines and procedures. The ingestion pipeline begins with the collection of these documents, followed by a series of processing steps that include extraction, normalization, and validation of entities. During the extraction phase, the system identifies relevant entities such as product names, roles, and procedures from the text. The normalization step ensures that the extracted entities are standardized, while the validation phase applies quality control measures to verify the accuracy of the data. Outputs from this DAG are stored in a data warehouse, allowing for efficient retrieval and analysis. Key performance indicators (KPIs) such as extraction accuracy and processing time are monitored to ensure the effectiveness of the workflow. The business value of this DAG lies in its ability to improve operational efficiency, reduce manual data entry errors, and enhance the accessibility of critical information within the retail organization.
Part of the SOPs & Playbooks solution for the Retail industry.
Use cases
- Increases operational efficiency by automating data extraction
- Reduces manual errors in document processing
- Enhances data accessibility for decision-making
- Improves compliance with standardized procedures
- Facilitates quicker onboarding of new employees with clear SOPs
Technical Specifications
Inputs
- • SOP documents in DOCX format
- • Playbook documents in PDF format
- • Existing entity databases for validation
Outputs
- • Extracted named entities dataset
- • Normalized entity records
- • Quality assurance reports
Processing Steps
- 1. Collect SOP and Playbook documents
- 2. Extract named entities using NLP techniques
- 3. Normalize extracted entities for consistency
- 4. Validate entities against existing databases
- 5. Generate quality assurance reports
- 6. Store results in a data warehouse
Additional Information
DAG ID
WK-0390
Last Updated
2025-06-22
Downloads
38