AI Chatbot & Knowledge Base Creation
Budget: $15 – $25 USD
1. Phase 1: Data Processing
- Data Extraction: Extract data from the OS Ticket system DB type: MYSQL).
- Data Cleaning: Remove duplicates, irrelevant entries, and inconsistencies.
- Data Transformation: Prepare data for AI processing.
2. Phase 2: Knowledge Article Generation
- Text Analysis: Use NLP models to analyze ticket content.
- Ticket Categorization: Group tickets by topics or issues.
- Article Creation: Automatically generate structured knowledge articles from categorized tickets.
3. Phase 3: Knowledge Base Organization
- System Design: Define structure and taxonomy for the knowledge base.
- Integration: Store articles in a searchable and intuitive format.
- Access Control: Implement user roles and permissions for the knowledge base.
4. Phase 4: Data Insights and Statistics
- Data Analysis: Identify trends, frequently asked questions, and critical areas for improvement.
- Visualization: Generate reports and dashboards for actionable insights.
5. Phase 5: FAQ Creation
- Content Extraction: Identify common issues and solutions from the knowledge base.
- Structure FAQs: Format them into concise, user-friendly questions and answers.
- Validation: Review and refine the FAQ content for accuracy.
6. Phase 6: AI Chatbot Development
- Bot Design: Define chatbot functionality and scope (e.g., ticket updates, FAQs).
- Bot with free customer text
- Bot according to workflow
- Model Training: Train the chatbot using the knowledge base and FAQs.
- Gives automatic replies for common issues
- Suggested replies from templates or the knowledge base
- Website and Software Integration: Embed the chatbot into the Elspec website, Jira, NetSuite, Confluence.
- Testing: Conduct extensive testing for user queries and edge cases.
7. Phase 7: Deployment and Maintenance
- System Deployment: Launch the knowledge base and chatbot on the live environment.
- Monitoring: Implement real-time monitoring for performance and accuracy.
- Ongoing Maintenance: Update the knowledge base, FAQs, and chatbot responses regularly.
- Data Extraction: Extract data from the OS Ticket system DB type: MYSQL).
- Data Cleaning: Remove duplicates, irrelevant entries, and inconsistencies.
- Data Transformation: Prepare data for AI processing.
2. Phase 2: Knowledge Article Generation
- Text Analysis: Use NLP models to analyze ticket content.
- Ticket Categorization: Group tickets by topics or issues.
- Article Creation: Automatically generate structured knowledge articles from categorized tickets.
3. Phase 3: Knowledge Base Organization
- System Design: Define structure and taxonomy for the knowledge base.
- Integration: Store articles in a searchable and intuitive format.
- Access Control: Implement user roles and permissions for the knowledge base.
4. Phase 4: Data Insights and Statistics
- Data Analysis: Identify trends, frequently asked questions, and critical areas for improvement.
- Visualization: Generate reports and dashboards for actionable insights.
5. Phase 5: FAQ Creation
- Content Extraction: Identify common issues and solutions from the knowledge base.
- Structure FAQs: Format them into concise, user-friendly questions and answers.
- Validation: Review and refine the FAQ content for accuracy.
6. Phase 6: AI Chatbot Development
- Bot Design: Define chatbot functionality and scope (e.g., ticket updates, FAQs).
- Bot with free customer text
- Bot according to workflow
- Model Training: Train the chatbot using the knowledge base and FAQs.
- Gives automatic replies for common issues
- Suggested replies from templates or the knowledge base
- Website and Software Integration: Embed the chatbot into the Elspec website, Jira, NetSuite, Confluence.
- Testing: Conduct extensive testing for user queries and edge cases.
7. Phase 7: Deployment and Maintenance
- System Deployment: Launch the knowledge base and chatbot on the live environment.
- Monitoring: Implement real-time monitoring for performance and accuracy.
- Ongoing Maintenance: Update the knowledge base, FAQs, and chatbot responses regularly.