Automate resolution suggestions and fixes with AI
Neo’s automated ticket resolution using AI combines intelligent analysis with autonomous execution to resolve support tickets faster and more accurately. By interpreting ticket context, historical resolutions, and linked documentation, Neo identifies the root cause of issues and recommends the best next action — or performs it automatically if approved. Integrated with tools like IT Glue, Hudu, and your PSA, Neo bridges the gap between insight and action, closing tickets with precision while maintaining complete documentation. This transforms reactive service desks into proactive, self-healing systems — improving first-time fix rates, reducing technician workload, and maintaining consistency across every customer interaction. Discover how Neo’s AI-driven resolution engine powers MSP automation at scale, ensuring faster responses and higher service quality throughout your IT operations.
Automated ticket resolution is the use of AI to analyze support tickets, match them to known fixes, and either suggest or execute the right remediation steps. For MSPs, it means faster responses, fewer escalations, and consistent outcomes—because common issues are identified, resolved, and documented automatically.
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.
