Wabby, the assistant
Product

Wabby.

Land Networking's industrial AI assistant. A language model with context, tools, memory and limits, deployed where the data lives and always answering with evidence.

A chatbot is not an assistant

A chatbot is a brilliant intern who has read every library in the world but has never set foot in your plant. The assistant is that same intern with the key to the documentation room, access to the SCADA, and the obligation to cite the source.

Chatbot

  • ✕Answers from memory
  • ✕Doesn't know which one is your station 4
  • ✕Never says “I don't know”
  • ✕Can't do anything

Assistant (Wabby)

  • ✓Retrieves your documents and your data
  • ✓Has access to SCADA, historian and tickets
  • ✓Cites a source for every answer, or doesn't answer
  • ✓Executes actions, only the allowed ones, under approval

Five layers, all yours

The model is the most famous part, but not the most important one. What matters is what surrounds it: your context, your tools, your permissions.

01Interface

Web chatTeamsHTML embedded in the HMIWhatsApp / Telegram / emailVoice and avatar

02Agents

Operator copilotAlarm diagnosisPerformance analysisMaintenanceThey call ML models already running on the platform

03Knowledge (RAG)

Document index with metadataPer-source permissionsCitations in every answer

04Connectors

SCADA: OPC UA · SQL · REST · GraphQL · MQTTERP · MES · CMMS · LIMS · QMSJira · Zendesk · ServiceNowSharePoint · Confluence · PDF

05Governance and security

On-prem, edge or cloudGDPRAudit trail for every answerRoles per plant and line

How it connects

Hover over (or tap) each box to see what it reads from there and through which protocol.

Embedded HTMLWhatsApp · Telegram · MailChatWabby avatarSCADAERPMESCMMSHistorianTicketingDocs and SOPVector DBindex · metadataAgents and guardrailsplans · verifiesLLMswappable
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Open-standard reads · writes only under human approval.

The model swaps out. What's yours stays.

Wabby runs on open-weights models (Qwen, Nemotron, Gemma, gpt-oss, Mistral…) deployed on your server or at the edge with Ollama or vLLM, or on a commercial API if your policy allows it. Swapping the model changes nothing above it: the context, permissions and citations are yours.

QwenNemotronGemmagpt-ossMistralCommercial API

In a few years nobody will buy “an LLM”. It'll be like electricity. What will hold value is the context.

sizecapabilityedge≈3–9B activeplant server≈27Bclusterlarge

No benchmark figures: they age in weeks.

Watch it work

Three demos under a minute, no voice-over, subtitled, recorded on the real demo.

Video in productionSOP-207 §4

From alarm flood to diagnosis

Screen with 200 alarms → card with three causes and the cited procedure.

Video in productionGraphQL · OEE_L3

Line 3's OEE

The question, the GraphQL query it generates, the chart it builds, the explanation.

Video in productionOT-4812

Maintenance with memory

IO-Link diagnostics of a valve → March ticket → proposed WO with an approval button.