Projects
Ayurs Infotech

NLP Model for Semiconductors - 42% Cost Reduction

Developed NLP model for semiconductor ticket management, cutting operational costs by 42% through streamlined automated ticket classification and routing.

3 min read

Impact

42% cost reduction, streamlined ticket management, automated routing

NLP
Text Classification
Automation
Semiconductors

Overview

Developed a specialized NLP model for semiconductor industry ticket management, achieving 42% cost reduction through intelligent automation. The system classifies and routes technical support tickets, streamlining operations and significantly reducing manual processing overhead.

Technical Architecture

NLP Classification Model

  • Multi-class text classification for ticket types
  • Technical domain understanding (semiconductor-specific terminology)
  • Intent recognition and severity assessment
  • Custom NER for component and issue identification

Automation Pipeline

  • Automated ticket intake and preprocessing
  • Intelligent classification and priority assignment
  • Automated routing to appropriate teams
  • Escalation logic for high-priority issues

Domain Adaptation

  • Training on semiconductor industry data
  • Technical terminology handling
  • Company-specific component catalogues
  • Historical ticket patterns

Key Features

  • Automated Classification: Instant categorization of incoming tickets
  • Intelligent Routing: Direct tickets to appropriate specialists
  • Priority Assessment: Automatic severity and urgency determination
  • Cost Optimization: Reduced manual triage and routing effort
  • Technical Understanding: Handles semiconductor-specific language

Technical Challenges & Solutions

Challenge: Technical Domain Complexity

Semiconductor industry has highly specialized terminology and complex technical descriptions.

Solution: Built domain-specific vocabulary from technical documentation and historical tickets. Fine-tuned pre-trained models on semiconductor corpus. Created custom NER for component names and issue types specific to the industry.

Challenge: Varied Ticket Quality

Tickets ranged from well-structured engineer reports to vague user complaints.

Solution: Implemented robust preprocessing handling varied input quality. Created confidence scoring system – high-confidence predictions automated, low-confidence flagged for review. Built feedback loop improving model on ambiguous cases.

Challenge: Class Imbalance

Some ticket types were much more common than others.

Solution: Used class-balanced training with weighted loss functions. Implemented oversampling for rare categories. Designed threshold optimization per class rather than global threshold.

Impact

  • 42% Cost Reduction: Dramatic decrease in operational costs through automation
  • Streamlined Operations: Faster ticket resolution through efficient routing
  • Improved Response Times: Immediate classification and routing
  • Better Resource Allocation: Specialists focused on resolution, not triage

Technologies Used

  • NLP: Transformers (BERT), spaCy, custom NER
  • ML: scikit-learn, text classification
  • Text Processing: Tokenization, technical terminology handling
  • Deployment: Model serving, monitoring
  • Languages: Python

Cost Reduction Breakdown

The 42% cost savings came from:

  • Reduced manual ticket triage time (primary factor)
  • Faster routing to correct teams (reduced hand-offs)
  • Better priority assessment (efficient resource allocation)
  • Decreased escalation cycles (accurate initial routing)

Innovation

  • Domain-Specific NLP: Tailored solution for semiconductor industry
  • End-to-End Automation: Complete pipeline from ticket intake to routing
  • Continuous Learning: System improved from feedback and new ticket patterns