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AI-Powered Customer Support Transformation Through Python Migration

Description

This project aimed to streamline customer support workflows using advanced AI and ML algorithms. The platform enables automation and optimisation of ticket management, sentiment analysis, escalation prediction, and response generation. The migration from Python 3.8.2 to 3.11.9 addressed critical technical debt, enhanced performance, and ensured future-proofing for scalability and maintainability.

Problem Statement

The project encountered several roadblocks due to the legacy Python version:

  • Compatibility Issues : Older libraries were reaching end-of-life support, leading to potential security and performance vulnerabilities.
  • Limited Performance : Computationally intensive tasks like image transcription and model inference were slower due to Python 3.8.2’s runtime limitations.
  • Technical Debt : Outdated syntax, deprecated features, and an increasing number of workarounds affected code readability and maintainability.
  • Scalability Challenges : The platform faced difficulties in adapting to modern development standards and growing data volumes.

Solution

A structured, phased approach was employed for the migration :

Phase 1 : Planning and Preparation

  • Analysis of Requirements : Identified dependencies, modules, and areas impacted by the migration.
  • Stakeholder Collaboration : Conducted meetings with QA, DevOps, and client teams to align on objectives and timelines.
Environment Setup -
  • Installed Python 3.11.9 on macOS using Homebrew.
  • Created isolated virtual environments for testing using venv.
  • Defined baseline performance metrics for pre-migration modules.

Phase 2 : Codebase Audit

Syntax Updates -
  • Replaced Python 3.8-specific type hints (List, Dict) with built-in types (list, dict).
  • Updated asyncio handling to match new loop management standards.
Dependency Analysis -
  • Used pip freeze to generate a list of libraries.
  • Cross-checked dependencies for Python 3.11 compatibility.
  • Highlighted critical libraries such as PyTorch, NumPy, Pandas, and TensorFlow.
  • Automated Audits : Leveraged pyupgrade to identify and refactor outdated syntax automatically.

Phase 3 : Dependency Management

Library Updates -
  • Upgraded dependencies using pip install --upgrade.
  • For deprecated libraries, identified and integrated alternatives (e.g., replacing unsupported utility libraries).
  • Testing Dependencies : Validated updated libraries in isolated environments to ensure compatibility and functionality.

Phase 4 : Comprehensive Testing

Unit Testing -
  • Modified and executed existing test cases to verify individual module functionalities.
  • Created additional test cases for edge scenarios introduced by library updates.
Integration Testing -
  • Simulated real-world workflows, including ticket escalation prediction, sentiment analysis, and tag recommendations.
  • Ensured seamless communication between modules post-migration.
Performance Testing -
  • Benchmarked runtime, memory usage, and throughput metrics for each module.
  • Compared pre- and post-migration performance, noting improvements of 10-15% in processing-heavy tasks.

Phase 5 : Deployment and Monitoring

Staging Deployment -
  • Deployed the upgraded codebase to a staging environment for QA validation.
  • Conducted user acceptance testing (UAT) to identify and resolve bugs or regressions.
Production Deployment -
  • Released the project to the live environment after successful validation.
  • Monitored the system for two weeks using Application Performance Monitoring (APM) tools, resolving any emergent issues.

Feature List

01
Escalation Prediction

Predicts tickets at risk of escalation using advanced ML models.

02
Sentiment Analysis

Analyses customer feedback to prioritise critical issues.

03
Image Transcription

Extracts actionable text from image data through OCR techniques.

04
Tag Recommendation

Suggests tags for better ticket organisation using historical data.

05
Response Suggestions

AI-driven response generation for support agents.

06
Ticket Summarisation

Summarises ticket details for faster comprehension.

Tech and Solution Stack

Programming Language

Python 3.11.9.

Libraries and Frameworks

Skilled in ML (PyTorch, TensorFlow), data handling, and testing (pytest, unit test).

Performance Enhancements

Optimized execution using Python 3.11's CPython runtime and JIT interpreter.

Development Environment

Used virtual environments and Docker for isolation, testing, and deployment.

Hosting

The project is hosted on a scalable cloud-based infrastructure(I am not very aware of the hosting platform this task is handled by the client's side):

Maintenance

  • Dependency Updates : Regularly monitor and update libraries to maintain compatibility and performance.
  • Performance Audits : CConduct periodic benchmarking and optimisations.
  • Bug Tracking : Use integrated tools to detect and resolve errors in real time.
  • Regression Testing : Monthly test cycles to ensure no functionality is impacted by updates.