Znuny

AI Attribute Classification for Znuny

Custom model for your Znuny queues — Full On-Prem training on your GPU, on-prem inference, zero ticket export.

  • Custom model trained on your exact Znuny queue definitions
  • Classify other attributes and ticket summaries
  • On-premise inference — no ticket content leaves your network
  • Full On-Prem trains on your GPU from your helpdesk setup — inference stays on-prem; ticket content never leaves your infrastructure.
  • Native Znuny integration via Generic Interface connector
Integration Flow

How It Works with Znuny

Step 1

Define Your Queues

Provide your Znuny queue names and short descriptions as a short setup list — no real tickets needed.

Step 2

Train Your Model

Full On-Prem trains on your GPU from setup descriptions — nothing leaves your infrastructure. Inference stays on-prem; ticket content never leaves. Typically same-day.

Step 3

Deploy On-Prem

Install OTAI Runtime Core via Docker Compose. Load your signed model artifact — inference runs entirely in your network.

Step 4

Go Live & Monitor

The Znuny connector reads new tickets via Generic Interface and writes routing decisions back to dynamic fields. Monitor and retrain as needed.

Custom Model Training

Custom Models for Attribute Classification

Full On-Prem trains and runs on your GPU — air-gap capable. Nothing leaves your infrastructure, not even setup data.

Full On-Prem Path

Your Infrastructure (On-Prem)
GPU required
OTAI Studio

Configuration for subscriptions and model delivery on your side

Znuny

New tickets arrive for AI processing

OTAI Runtime + Connector GPU required

Local training and inference on your GPU — air-gap capable

Tickets Enriched by AI

Classification, priority, and fields updated automatically

No ticket data leaves your network. Full On-Prem trains entirely on-prem — nothing leaves, not even setup data. Inference always runs on your infrastructure.

AI Capabilities

Fields AI Can Improve in Znuny

OTAI doesn't just route to Queue — it can classify and populate multiple Znuny fields automatically.

Queue

Queue Assignment

Automatically route tickets to the correct Znuny queue based on content analysis

Priority

Priority Classification

Set ticket priority by analyzing urgency signals in subject and body

Type

Type Classification

Classify tickets by tone (Incident, Service Request, Change, etc.)

State

Status Prediction

Predict initial ticket state based on request tone

Dynamic Field

Custom Field Population

Fill Znuny dynamic fields with AI-extracted structured data

Requirements

Znuny 7.0+ (LTS recommended)
Docker / Docker Compose for OTAI Runtime
Generic Interface web service enabled
2 CPU cores, 4 GB RAM minimum

Common Znuny Setups

Pre-built setup templates to get started quickly with Znuny.

1

IT Service Desk

Incident, Service Request, Change, Problem, Knowledge Management

2

Managed Service Provider

Per-client queues, SLA tiers, escalation routing

3

Government/Public Sector

Citizen requests, inter-department routing, compliance tracking

ROI Estimate

See Your Potential Savings

Example monthly savings from replacing manual ticket routing with OTAI. See the full page for the assumptions behind these figures.

Quick ROI Estimate

See how much Znuny can save you

Small team (50 tickets/day)€3,500
Mid-size (200 tickets/day)€13,600
High volume (750 tickets/day)€54,500

estimated monthly savings vs. manual routing

See full ROI examples
Pricing

Editions

Free Cloud Trial to evaluate. Full On-Prem: from ~€500 / month (software, agent-based), on request — quote depends on agents + pack (Entry / Standard / Premium).

Free Cloud Trial

Evaluate & test

Free
  • Routing, priority, multi-field AI + summaries
  • No field-count limit
  • Hosted Free Cloud Trial — runs online, not on your servers
  • Built to evaluate before Full On-Prem
  • Community support
Available Now

Full On-Prem

On request · agent-based software

From ~€500 / month
  • Routing, priority, multi-field AI + summaries
  • Unlimited values per field
  • Quote depends on agents + pack (Entry / Standard / Premium)
  • Run on your own GPU — bring your own or optional appliance
  • Requires a GPU with at least 24 GB VRAM
  • Fully isolated — nothing leaves, not even setup data
  • Maximum data sovereignty & compliance
  • Support & updates included
View Product

Frequently Asked Questions

Common questions about using OTAI with Znuny.

Ready to automate classification
for Znuny?

Get a custom model trained on your Queue definitions. Same-day turnaround, on-prem deployment.