A Lightweight, Auditable Framework for Categorizing Consumer Communications in Restricted Environments
By: Rishi Bharaj
Overview: This project introduces a lightweight, auditable framework for classifying consumer communications using a structured keyword-driven approach. It’s built to thrive in environments with high data confidentiality and tooling restrictions—primarily using Microsoft Excel as the base platform. No code, no external dependencies, just logic, rigor, and practical design.
Motivation: Manual interpretation of unstructured consumer correspondence can be inconsistent and error-prone, especially in regulated domains. When access to advanced tooling is restricted, teams need scalable, interpretable systems that don't compromise on quality or auditability.
This framework:
• Simulates intent detection using rule-based keyword mapping
• Derives structured outcomes from emails, SMS, and scanned documents
• Applies statistical validation and confidence scoring for outcome reliability
Disclaimer: Due to the proprietary nature of the framework, no client data, keywords, or classification categories are shared. All examples provided are illustrative and designed to convey methodology only.
Core Components
• Excel-based Keyword Mapping Engine
• Outcome Precedence Logic for multi-category detection
• Confidence Level Sampling (99% for high-priority, 95% for general categories)
• Variance & Distribution Analysis using statistical formulas
• Performance Optimization Tips for large-scale audits
Visual Illustrations: Sample logic sheets demonstrate:
• Keyword-category mappings
• Category detection layers
• Audit sampling calculations
• Keyword heat maps for distribution analysis
(Note: All data shown is anonymized and synthetic)
Applicability:
Ideal for:
• Quality Assurance and Operations teams in restricted digital ecosystems
• Environments with Excel as the only permitted toolset
Roadmap:
Planned enhancements include:
• Expanding category granularity
• Refining precedence logic for edge cases
Contribution: Due to data sensitivity, external contributions are currently limited to methodology feedback, optimization suggestions, and usage scenarios. Feel free to open an issue or start a discussion!