Matrix42 MCP Server: Ask Your Service Desk in Plain Language
An open-source MCP server that lets Claude, Copilot or Cursor work with Matrix42: the API, the data model and the service desk. Read-only by default.
Actionable guides, case studies and thought-leadership on turning data into decisions, automating processes and scaling intelligently - straight from the S&S team.
An open-source MCP server that lets Claude, Copilot or Cursor work with Matrix42: the API, the data model and the service desk. Read-only by default.
An open-source MCP server that lets Claude, Copilot or Cursor work with Matrix42: the API, the data model and the service desk. Read-only by default.
How an n8n workflow checks Microsoft Entra ID app registrations for expiring secrets and certificates and warns you before an integration breaks.
How to find anonymous access and public links in Confluence spaces before sensitive documentation becomes readable from outside.
Pages owned by people who have left lose their accountability. How to find Confluence pages with inactive owners automatically and reassign them.
Outdated Confluence pages are an audit risk. How to detect stale content automatically with n8n and get a report of the pages to review.
Stale accounts and unchecked permissions add up. How automation keeps identities and access in order from onboarding to offboarding.
Why data and identity governance matters for companies of every size and why it is easier to set up early than to retrofit later.
OpenAI's gpt-oss-120b and gpt-oss-20b are open-weight models you can run yourself. What they mean for AI agents that work without a cloud API.
Machine learning predicts, generative AI creates. The difference in plain terms and which business tasks suit each in a small or mid-sized company.
A plain-English introduction to artificial intelligence for SME leaders: what it is, where it comes from and where it pays off in daily business.
Clever prompts make good demos, not reliable processes. What AI in production needs besides: retrieval, fine-tuning and workflows around the model.
How LoRA and QLoRA fine-tune large language models with a fraction of the GPU memory and what that means for the cost of custom models.
Retrieval-augmented generation or fine-tuning? When to let a language model look up your data and when to train it, with examples from IT and ERP.
n8n or Make? A comparison of pricing, GDPR and AI options and why self-hosted n8n with local models can cut costs and latency.
n8n or Zapier? A comparison of pricing, GDPR, AI costs and hosting and what changes when n8n runs on your own server with Ollama.
n8n-nodes-matrix42 connects Matrix42 to n8n: installation, credentials, the operations of the node and an end-to-end example.
What an invoice-processing agent costs with a cloud LLM API and with a small model on your own GPU, calculated on a real workflow.
Copying data between ERP and spreadsheets by hand costs more than it seems. Where the hidden costs are and how automation with n8n removes them.
Most AI agent tasks run well on models under 10 billion parameters. Why small language models on Ollama and n8n cut costs and keep data in-house.
How to turn PDFs and documents into a chat that answers with sources: ingestion, embeddings, vector store and upkeep of a RAG knowledge base.
Why cloud LLMs are a GDPR risk for European companies and how running language models locally keeps data, costs and latency under control.
Step by step: choose a model, check your hardware, install Ollama and connect it to n8n to run language models on your own machines.