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Cost + capacityTRAVEL TECHNOLOGY / ITProject 03

AI IT Support Resolution Search

An IT support search system that turns a ticket description into an evidence-backed resolution starting point while technicians retain control.

Target15-25%Ticket deflectionCount eligible requests resolved through guided self-service without technician assignment.
Target+10-18ppSLA attainment improvementCompare matched ticket categories and priority bands before and after deployment.
Modeled20-30%Backlog reductionModel backlog change from deflection, faster triage, and assisted resolution rates.

The business problem

The information required to resolve an IT ticket was distributed across a knowledge base, internal documentation, previous incidents, and team conversations.

Technicians tried several keywords and sources before they could understand the issue. Repeated information discovery increased backlog and threatened response and resolution SLAs.

  • IT Service Desk
  • Infrastructure
  • Security Operations
  • Application Support

How the work changes

Today

  1. 01Ticket arrivesA technician reads the issue and identifies possible systems involved.
  2. 02Search manuallyKnowledge bases, old incidents, and conversations are queried separately.
  3. 03Test relevanceThe technician checks whether a prior answer applies to the current environment.
  4. 04Begin resolutionTroubleshooting starts only after enough context has been reconstructed.

With the system

  1. 01ClassifyAI identifies the affected service, urgency, and likely issue family.
  2. 02FindRelated incidents, fixes, and runbooks are retrieved.
  3. 03GuideA grounded troubleshooting path is prepared for the technician.
  4. 04ResolveThe technician validates the evidence and completes the action.

What the system does

Natural-language AI search surfaces relevant fixes, runbooks, and prior incidents directly from the incoming ticket.

  • Use the ticket description as a natural-language search query.
  • Retrieve approved runbooks, previous incidents, and system documentation together.
  • Show evidence and environment context next to each suggested resolution path.
  • Keep the technician responsible for diagnosis, action, and closure.

What this depends on

Historical tickets contain enough resolution detail to identify reusable patterns.

Runbooks have named owners and known applicability boundaries.

Deflection is limited to low-risk and well-understood request categories.

Start with clarity

If the business case isn’t there, we’ll tell you before you build.

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