Fixify innovations
December 1, 2025
4
min read

Triage: Bringing order to the ticket queue

Peter Silberman

Table of contents

Example H2
Example H3
Share this post:

Listen to this blog as a podcast

00:00

It’s 7:07 AM, your coffee’s still hot, and you’re logging on to start your day as an IT analyst. The queue’s already packed. Each ticket has its own quirks. One ticket needs a green light from Company A’s IT manager. Another’s fair game for auto-resolution. A third … well, it depends who you ask. Across customers, no two rule sets are the same and remembering them all is next to impossible. 

So we asked ourselves: what if analysts didn’t have to remember every rule? What if the system simply knew them and made the right call in real time?

That’s why we built Fixify Triage. It’s an AI-powered engine designed to reduce cognitive load, streamline decision-making, and boost both speed and accuracy across the ticket queue.

What does it do?

The triage queue is the first stop for every ticket at Fixify. When a new request comes in from a customer our analysts review it and determine — based on customer-specific instructions — whether Fixify should tackle it or leave it to the customer. We call this a triage decision.

But those customer instructions aren’t static; they change frequently (and usually at the worst possible time). For each use case, the rules vary:

  • Which tickets can Fixify handle independently?
  • Which should stay with the customer?
  • Who approves specific requests?
  • Which needs immediate escalation?

Managing this level of variability makes it hard to scale decision-making consistently. That’s why we built Triage Guidelines into the Fixify Knowledge Hub. They capture those ever-changing rules in plain English. Then, they apply them in real time as each new ticket pops up on an analyst’s screen.

Fixify Triage reads the context of each incoming ticket and automatically applies customer-specific context, surfacing an instant recommendation:

  • “In Scope” → Fixify should handle this.
  • “Out of Scope” → This belongs to the customer.
  • “Unknown” → Needs human review.

Each suggestion includes a justification with reference to source documentation. Since Fixify includes a human in the loop, the analyst can validate the triage decision or adjust in real time.

It’s powered by a Retrieval-Augmented Generation (RAG) system that fetches the most relevant customer instructions in real time, applies them through an AI model, and returns a decision in seconds.

The payoff is clear. Analysts no longer memorize dozens of custom rules. Decisions happen faster, with more consistency and transparency. Cognitive load drops — accuracy goes up.

What’s cool about AI-Powered Triage

Real-time guidance Recommendations appear instantly in the triage queue — no tab-hopping or manual lookups.
Transparent reasoning Every suggestion includes an explanation and source link, so analysts always understand the “why.”
Built-in feedback loop Analysts can accept or override AI decisions. We use that feedback data to revise the predictions, improving accuracy over time.
Speed at scale Responses clock in at typically under 4 seconds per ticket to keep up with live triage workflows (thanks to Groq-accelerated infrastructure).

What makes it harder than it looks?

Every IT environment is uniquely messy and often deeply custom, so we needed to flex accordingly. But flexibility brings complexity — especially when deciding which tickets our analysts should take on. Here’s a look under the hood at the technical must-haves that make it all click.

1. Rules that never sit still

Customer-specific instructions are living documents — constantly changing as environments evolve. We needed a way to store them in a structured form, but also retrieve and apply them dynamically.

That’s why we built the Knowledge Hub. It’s a place to capture customer-specific rules and guidance. Every time a ticket arrives, we embed key context (title, body, classification) and match it against the customer’s latest rules to ensure the guidance is always fresh.

2. Balancing latency and context

Analysts can’t wait 30 seconds for a model to think. But triage decisions also depend on large amounts of contextual information.

We had to tune for both: selecting a model with a large enough context window and optimizing the RAG pipeline for sub-2-second responses.

3. Trust through transparency

We’re making black-box decision-making a thing of the past. Every recommendation includes traceable reasoning. Analysts see the logic, validate it, and they can adjust rules instantly. That transparency builds trust and also improves training data quality.

4. Keeping humans in the loop

AI isn’t making final calls — people are. Every analyst decision (accept or override) is logged and compared to model output. This gives us a running F1 score of 0.8+ — a key accuracy metric showing strong alignment between AI and analyst decisions. It also powers a continuous feedback loop that sharpens performance over time.

Closing thoughts

AI-Powered Triage started as a way to help analysts untangle ticket queues. Since then, we’ve evolved it into something bigger: a foundation for decision-making at scale.

It gives our analysts a running start at every ticket by combining human judgment with AI’s precision, Fixify ensures every ticket lands in the right hands instantly, intelligently, and transparently.

Get a demo to see us in action.

Related articles

IT help desk best practices
6 min read

Outsourcing IT support services without losing control: Here’s how we do it

Molly Small
May 28, 2025
Outsourcing IT support services without losing control: Here’s how we do it
IT help desk best practices
5 min read

The hidden cost of untracked IT work (and how to fix it)

Molly Small
September 22, 2025
The hidden cost of untracked IT work (and how to fix it)
IT 101
4 min read

What is IT service management?

Molly Small
June 23, 2025
What is IT service management?
Fixify innovations
Fixify innovations

Stay in the loop

in action

Sign up to get notified about our latest news and blogs