Ticket Automation Planner — Pick Your Best Path

Modernize ticket routing fast—no matter where you start. This planner helps you choose the right path based on your data reality: lots of labeled tickets, lots of unlabeled tickets, or almost no data. Each path ends in a concrete service package with clear deliverables and KPIs, so you can move from idea → pilot → production without guesswork.

Who this is for: IT/service teams on Znuny/OTRS/OTOBO (or similar) who want reliable queue/priority/tag predictions, either on-prem or via a hosted API.

What you’ll get: short decision flow, 4 actionable paths (A–D), add-ons (multilingual, extra attributes), gates/metrics to know when you’re ready, and a data-readiness checklist.

How to use this page

• Start with the one-screen overview and answer three questions: Labeled? → Unlabeled? → Fast?
• Click the box for Flow A/B/C/D to jump to its steps, deliverables, and KPIs.
• Use the add-ons if you need multiple languages or more outputs (tags, assignee, first answer).
• Keep the gates tight (per-class F1 + business KPIs) so pilots translate to production confidence.

Now continue with the overview diagram and the detailed flows below. Nice—here’s a fuller write-up you can drop under your diagrams. I kept it skimmable but added real guidance and thresholds so readers can confidently pick a flow.

Got it — I’ll keep your new short diagrams and add clear, concise explanatory text for each section so the article feels complete while still being easy to scan.

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0) One-screen overview

---
config:
  theme: 'dark'
---
flowchart TD
  S((Start)) --> Q1{Labeled?}
  Q1 -- Yes --> A0[Flow A]
  Q1 -- No --> Q2{Unlabeled?}
  Q2 -- Yes --> B0[Flow B]
  Q2 -- No --> Q3{Fast?}
  Q3 -- Yes --> D0[Flow D]
  Q3 -- No --> C0[Flow C]

click A0 "#flow-a-many-labeled" "Flow A"
click B0 "#flow-b-many-unlabeled" "Flow B"
click C0 "#flow-c-few-or-no-tickets" "Flow C"
click D0 "#flow-d-quick-start-hosted-api" "Flow D"

How to use this overview: Start at the top, answer the questions, and follow the branch to your matching flow. Click a flow to see its details.

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Flow A — Many labeled tickets

---
config:
  theme: 'dark'
---
flowchart TD
  A[Audit/Tax] --> B[Train]
  B --> C[Eval]
  C --> D[On-Prem]
  D --> E[Pilot]
  E --> F[Support]

When to choose this:

• You already have thousands of tickets with queue, priority, or tag labels.
• You want a custom-trained model for maximum accuracy.

What happens in this flow:

1. Audit/Tax — Check label quality, class balance, and naming.
2. Train — Fine-tune the classification model with your data.
3. Eval — Measure per-class precision/recall/F1.
4. On-Prem — Deploy inside your own infrastructure.
5. Pilot — Test in production with monitoring.
6. Support — Iterate and retrain as needed.

Recommended package: Fine-Tune + On-Prem Install.

---

Flow B — Many unlabeled tickets

---
config:
  theme: 'dark'
---
flowchart TD
  A[Ingest] --> B[Auto-Label]
  B --> C[QC]
  C --> D{OK?}
  D -- No --> B
  D -- Yes --> E[Train]
  E --> F[Eval]
  F --> G[On-Prem]
  G --> H[Support]

When to choose this:

• You have large historical ticket archives but no labels.
• You can allocate some human review time for quality checks.

What happens in this flow:

1. Ingest — Collect tickets from your system.
2. Auto-Label — Use LLM-assisted auto-labeling.
3. QC — Spot-check & correct samples.
4. OK? — Loop until quality meets threshold.
5. Train — Fine-tune with the curated set.
6. Eval / On-Prem / Support — Same as Flow A.

Recommended package: Auto-Label + Fine-Tune.

---

Flow C — Few or no tickets

---
config:
  theme: 'dark'
---
flowchart TD
  A[Define Tax] --> B[Synth Data]
  B --> C[Baseline]
  C --> D[Eval]
  D --> E{Pilot}
  E -- API --> H[Hosted DE]
  E -- Local --> I[On-Prem]
  H --> J[Collect]
  I --> J
  J --> K[Fine-Tune]
  K --> L[Prod/Support]

When to choose this:

• You're starting from scratch or have too few tickets to train on.
• You want a cold-start solution to go live quickly.

What happens in this flow:

1. Define Tax — Decide queues, priorities, tone.
2. Synth Data — Generate realistic tickets (DE/EN).
3. Baseline — Train initial model on synthetic data.
4. Eval — Check performance before rollout.
5. Pilot — Choose Hosted API for speed or On-Prem for control.
6. Collect — Gather real tickets during pilot.
7. Fine-Tune — Merge real + synthetic data.
8. Prod/Support — Go live with ongoing iteration.

Recommended package: Synthetic Cold-Start.

---

Flow D — Quick start via Hosted API

---
config:
  theme: 'dark'
---
flowchart TD
    A[Use API DE] --> B[Measure]
    B --> C{Tax OK?}
    C -- Yes --> D[Scale/Support]
    C -- No --> E[Auto/Synth - Train]
    E --> F[On-Prem]

When to choose this:

• You need results immediately.
• You want to try automation without training first.

What happens in this flow:

1. Use API DE — Instant classification via hosted German model.
2. Measure — Track routing, SLA, backlog impact.
3. Tax OK? — If satisfied, scale usage; if not, go to Flow B or C for training.

Recommended package: Hosted API Pilot → Fine-Tune (optional).

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Optional add-ons

Multilingual expansion

---
config:
  theme: 'dark'
---
flowchart TD
  A[More langs?] --> B{Hist per lang?}
  B -- Yes --> C[Auto-Label]
  B -- No --> D[Synth]
  C --> E[Train Multi]
  D --> E
  E --> F[Pilot/Eval]

Add support for additional languages via multilingual auto-labeling or synthetic generation, then train and evaluate per locale.

---

Extra attributes

---
config:
  theme: 'dark'
---
flowchart TD
  A[Add tags/assignee/FAA] --> B[Extend labels/gen]
  B --> C[Multi-task/Chain]
  C --> D[Deploy]

Predict more than queues/priorities — e.g., tags, assignee, or first answer time — by extending labeling and training a multi-task model.