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Znuny

AI Attribute Classification for Znuny

Queues, attributes, chatbot — on your GPU. Nothing leaves your network.

  • Znuny Queues
  • On-prem GPU
  • Chatbot
  • No data export
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Constanze Krishnaratne

Your host

Constanze Krishnaratne

CEO

Book AppointmentEmail usConnect on LinkedIn
How it works

Go live with Znuny

Connect the helpdesk, describe your setup in Studio, classify tickets zero-shot, run Full On-Prem.

01

Connect Znuny

02

Describe Queues

03

Configure Studio

04

Run On-Prem

  1. 01

    Connect Znuny

    Enable Generic Interface and connect OTAI with a web-service token.

  2. 02

    Describe Queues

    Use queue names and descriptions from your helpdesk setup — no ticket history.

  3. 03

    Configure Studio

    Zero-shot classification for queues, priority, and dynamic fields.

  4. 04

    Run On-Prem

    Runtime classifies new tickets locally and writes results back to Znuny.
On-Prem Classification

Classify Ticket Fields on Your Infrastructure

Full On-Prem AI runs inside your network to set queues, groups, priorities, and custom fields securely.

Full On-Prem Path

Your Infrastructure (On-Prem)
GPU required
OTAI Studio

On-prem configuration and control in your environment

Znuny

New tickets arrive for AI processing

OTAI Runtime + Connector

Local classification on your GPU — air-gap ready

Tickets Enriched by AI

Routing, priority, and custom fields updated automatically

No ticket data leaves your network. Everything runs on your infrastructure.

AI Capabilities

Fields AI Can Improve in Znuny

Queue

Queue Assignment

Priority

Priority Classification

Type

Type Classification

State

Status Prediction

Dynamic Field

Custom Field Population

Requirements

Znuny 7.0+ (LTS recommended)
Docker / Docker Compose for OTAI Runtime
Generic Interface web service enabled
2 CPU cores, 4 GB RAM minimum
GPU with 24 GB+ VRAM for Full On-Prem production
Start with the Free Cloud Trial if GPU hardware is not yet available
Quick Start

Common Znuny Setups

What a typical rollout looks like. We map your Queues in Studio; Runtime classifies new tickets and writes queue, priority, and dynamic fields back to Znuny.

IT Service Desk

  • Ticket: “Printer on 2nd floor is jammed again”
  • Writes Queue: Incident::Workplace
  • Sets Priority: 2 and type: Incident
  • Same pattern for service requests, changes, and problems

Managed Service Provider

  • Ticket: “Client ACME — VPN gateway unreachable”
  • Writes Queue: ACME::Network
  • Sets Priority: 4 from the client SLA
  • Same pattern for per-client queues and escalation paths

Government / Public Sector

  • Ticket: “Building permit for garage extension”
  • Writes Queue: Building Authority
  • Sets Priority: 2 and deadline field from the request type
  • Same pattern for citizen requests and inter-department routing
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 quotes based on agents and tier.

Free Cloud Trial

Evaluate & test

Free
  • Pre-configured GPU environment with OTAI installed
  • Includes sample ticket system and OTAI Studio
  • Instant access to evaluate features
  • No local hardware required
Instant setup

Full On-Prem

On request · agent-based software

From ~€500 / month
  • Studio and Runtime on your infrastructure
  • Chatbot, classification, summaries, and AI workflows
  • Flexible agent licensing: Entry, Standard, or Premium
  • Includes updates, security patches, and support
  • Run on existing hardware or partner hardware
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FAQ

Frequently Asked Questions

Questions about using OTAI with Znuny.

No. Full On-Prem classification is driven by your helpdesk setup — not by uploading historical ticket archives to an external service.

What OTAI uses instead:

  • Znuny queue names, descriptions, and keywords
  • Live ticket fields during inference on your OTAI Runtime host
  • Generic Interface write-back for predicted attributes

That architecture fits teams migrating from OTRS-era setups who already treat queue definitions as the authoritative routing catalogue. Ticket content stays inside your network throughout inference.

Read the Setup-based classification guide and the OTOBO/Znuny plugin setup for connector details.

Sources
Documentation

Documentation

Deploy, configure, and run OTAI with Znuny.

OTAI Runtime Docs

Setup, connectors, and configuration for Znuny.

Read Runtime Docs

OTAI Studio Docs

Configure workflows in the OTAI Studio control plane.

Read Studio Docs

Ready to automate classification
for Znuny?

Automate ticket classification on-premise using your existing Queue setup.

View Product
Constanze Krishnaratne

Your host

Constanze Krishnaratne

CEO

Book AppointmentEmail usConnect on LinkedIn