AI-based Assistant for Business Process Outsourcing
summary
Describes a deployed BPO ASSISTANT that auto-assigns tickets, predicts attendant accuracy and skill progression, and suggests actions to speed resolution. Uses historical ticket logs, TF-IDF and learned text features, feature fusion plus simple neural and regression models for suggestions, and a linear-programming allocator solved with a Python solver (pulp). Includes API ingestion, per-ticket confidence, feedback-to-retraining loop, dashboards for acceptance and drift alerts, and reports showing improved workload balance and rising attendant proficiency. ticket allocation via linear programming TF-IDF + feature fusion feedback-driven retraining
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chapters (15)
- 0:00Introduction to BPO and AI
- 2:04Understanding Business Process Outsourcing
- 3:51Challenges in BPO and AI Solutions
- 4:53AI Model for Ticket Allocation
- 6:20Technical Architecture Overview
- 8:51Data Requirements and Pipeline Setup
- 10:18Optimization Model Details
- 12:29Ticket Suggestion Model Training
- 14:19Production Environment and Feedback
- 15:26Operation Monitoring and Reports
- 18:22Feedback Integration and Model Retraining
- 21:41Key Insights from Business Understanding
- 24:50Q&A: Linear Programming Approach
- 26:00Q&A: Onboarding New Employees
- 27:35Q&A: GDPR Compliance
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