Accelerate resolution times with intelligent solution suggestions
Neo's Ticket Resolution Suggestion feature leverages AI to analyze incoming tickets and recommend solutions based on your internal documentation and historical resolution data. By searching through your knowledge base (from systems like IT Glue or Hudu) and examining past ticket resolutions, Neo can identify relevant procedures, technical documentation, and proven fixes for common issues. This capability significantly accelerates resolution times, improves first-time fix rates, and ensures consistent application of best practices across your service desk.
Connect Neo to your knowledge base sources such as IT Glue or Hudu. Navigate to the Integrations section in your Neo dashboard and select your knowledge base provider. Enter the credentials and authorize the connection.
In Neo's "L1 Engineer" action, you can choose the setting for Neo to suggest a ticket resolution for the technician.
Neo will analyze your knowledge base and historical ticket resolutions to train its AI model. The more data it can analyze, the more accurate it becomes. You can also manually review and provide feedback on suggested solutions during this training phase.
Once training is complete, activate the Ticket Resolution feature. Neo will begin automatically suggesting solutions for new tickets. Monitor the accuracy in the dashboard and regularly review performance metrics like solution acceptance rate and time saved to continuously optimize the system.
The quality and organization of your knowledge base directly impacts solution quality. Well-documented procedures with clear steps yield the best results.
Expect a 2-3 week training period for optimal performance. During this time, technician feedback is crucial for improving accuracy.
Consider standardizing your knowledge base structure and tagging system to improve the AI's ability to find relevant solutions.
Encourage technicians to document new solutions clearly. The system will incorporate these into future recommendations.
