AI operations2024 - Present

AI Ticket Triage

GPT-powered Freshservice incident triage that categorizes tickets, detects catalog requests, writes troubleshooting notes, and drafts requester responses before an agent begins work.

5K+

tickets processed yearly

420+

hours saved yearly

3

category levels

750+

projected phase 3 hours

01

How It Works

Every new support ticket is intercepted, summarized, classified, and enriched with private agent notes. The system coordinates focused helper flows for categorization, catalog matching, access detection, internal guidance, and public replies.

  • Three-level categorization using strict taxonomy matching
  • Catalog item detection for application access requests
  • Private HTML troubleshooting notes generated for agents
  • Requester-facing replies drafted from the ticket context

02

Model Selection

The current flow uses OpenAI with structured lookup tables for the service catalog and category hierarchy, keeping the model constrained to approved labels and public-safe response formats.

03

Roadmap

Phase 3 targets automatic resolution for repetitive issues such as MFA resets, lost phone workflows, reopened ticket cleanup, and common access problems with human escalation for ambiguous cases.

Technologies

What powered it.

OpenAIFreshserviceWorkatoService CatalogPrompt EngineeringHTML NotesREST APIs

Workflow visual

Freshservice AI intake, step by step.

A public-safe walkthrough of the two intake patterns: new incidents that need triage and reopened tickets that need a close-or-continue decision.

Workflow

New incident triage path.

1

Trigger

Freshservice sends a new incident

A real-time webhook delivers the ticket ID, subject, description, requester context, catalog list, and category hierarchy.

Ticket IDSubjectDescriptionRequesterCatalogTaxonomy
2

Context

Automation builds the AI packet

The ticket is wrapped with approved lookup data so the model can classify against known labels instead of inventing new ones.

3

Helper flows

Focused AI helpers analyze the request

Separate helper flows handle categorization, catalog matching, access detection, internal troubleshooting guidance, and requester messaging.

CategorizeCatalog matchAccess intentIT noteUser reply
4

Decision

The flow decides the ticket path

The output separates normal IT work, access requests that need catalog links, duplicate or missing context, and non-IT redirects.

5

Write-back

Freshservice gets structured notes

The ticket is updated with category fields, detected applications, a private agent note, and a public HTML reply.

L1-L3 categoryCatalog appsPrivate notePublic reply

Samples

Pick a ticket and see what the flow writes.

OpenAI helper flows

Ticket sample

Requester needs access to Docker Desktop and Zoom Business for a new dev setup.

Context read

Two application names appear in the description and both have catalog matches.

Category

Application Access / Request / Multi-App Access

Catalog match

Docker Desktop, Zoom Business

Access

Yes - new access request

Private agent note

Confirm catalog availability and guide the requester to the approved access request paths instead of manual provisioning.

Public requester reply

Please submit access through the catalog links for Docker Desktop and Zoom Business so approvals are captured.

Result

Requester gets the right self-service path immediately, while IT avoids a manual back-and-forth.