Test and score your AI support ticket classifier's accuracy

Runs your AI ticket classifier against known-correct examples and scores how accurate it is.

How the work actually flows

A straight line. Runs once per each test ticket with a known answer.

Pattern: Multiple Instances with a priori Design-Time Knowledge (13)

flowchart TD trig(("someone runs an accuracy test")):::human s0[["run each test ticket through classifier"]]:::mi s1["compare result to known answer"]:::task s2["calculate accuracy score"]:::task s3(("review score breakdown")):::human trig --> s0 s0 --> s1 s1 --> s2 s2 --> s3 out[/"accuracy score with error breakdown"/]:::out pay{{"confidence in classifier before changes"}}:::pay s3 --> out out --> pay classDef task fill:#e7f6fe,stroke:#34b8f0,color:#2c2a29 classDef svc fill:#f6f8fa,stroke:#7c8795,color:#2c2a29 classDef mi fill:#e7f6fe,stroke:#0079a8,color:#2c2a29,stroke-width:2px classDef human fill:#fff,stroke:#0079a8,color:#0079a8 classDef store fill:#f6f8fa,stroke:#0079a8,color:#2c2a29 classDef trig fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigtime fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigdata fill:#8ad4f5,stroke:#0079a8,color:#06314c,font-weight:bold classDef gate fill:#fff,stroke:#e8a23d,color:#6b4708,font-weight:bold classDef out fill:#1f9d6b,stroke:#167a53,color:#fff,font-weight:bold classDef pay fill:#06314c,stroke:#021f33,color:#fff
A stepRuns once per itemA personResultPayoff
Build size
Advanced

A larger build with multiple systems, AI reasoning, and custom rules.

Business functions
AI Agents & Autonomous SystemsAPI & Webhook IntegrationCustomer Support & Ticketing
Connects
OpenAI

The problem it solves

You roll out an AI tool to classify support tickets and it looks great in your initial tests, but you have no easy way to tell if it's still accurate after a prompt change or months of real traffic. Problems only surface once customers complain about being misrouted.

Who it fits

Support or ops teams running an AI classifier on incoming tickets who need to trust its accuracy.

How it works

  1. A set of test tickets with known correct answers is fed through the AI classifier
  2. The classifier tags each ticket by category and urgency, same as it would in production
  3. Each result is checked against the known correct answer
  4. A score is calculated for how many the classifier got right
  5. You review the scores to see where it's weak before changing the prompt
What you get

Weak spots in your classifier found before customers do

You get a clear accuracy score for your AI ticket classifier, showing exactly where it needs a better prompt before you trust it.

What you get

An accuracy score and a breakdown of which tickets the AI classifier got wrong.

What you need

An OpenAI API key and a data table or spreadsheet of test tickets with known answers.

We can build this. But should you?

The hard question is not how to build it. It is whether this is the right thing to build first.

That is what a Fractional Chief AI Officer figures out with you, before anyone writes a line of code.

Let's Talk Strategy

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