What is n8n and how is it different from Zapier or Make?

n8n is an open-source workflow automation tool. Unlike Zapier or Make, n8n can be self-hosted \u2014 which means your data stays on your infrastructure, not a third-party server. It supports custom code, API connections, and LLM integrations out of the box. For teams with data sovereignty requirements, self-hosted n8n is often the only viable option.

What is an agentic workflow?

An agentic workflow is an automation where an AI model doesn't just process input \u2014 it evaluates conditions and decides what happens next. For example: an email comes in, an LLM classifies the intent, and depending on the result, a different downstream action is triggered. Agentic workflows require clear guardrails, because the AI is making decisions, not just transforming text.

What is prompt engineering and why does it matter for automation?

Prompt engineering is the practice of structuring input to a language model to produce reliable, predictable output. In automation contexts, prompts are not one-off experiments \u2014 they run at scale, on every trigger. A poorly written prompt that fails 10% of the time is a production problem. Good prompt engineering reduces failure rates and makes LLM output trustworthy enough to act on automatically.

What are guardrails in LLM-based workflows?

Guardrails are constraints applied to a language model's behavior: instructions that tell the model what to do when it doesn't know something, how to format output, and what topics or actions are out of scope. In production workflows, guardrails prevent hallucination from propagating downstream \u2014 into a database, a Slack message, or a customer-facing response.

What is data sovereignty and why does it matter for AI workflows?

Data sovereignty refers to the principle that data is subject to the laws and governance frameworks of the country in which it is stored or processed. For teams in the EU, this means understanding which AI services process data outside GDPR jurisdiction \u2014 and structuring workflows accordingly. Using a self-hosted n8n instance with a locally deployed LLM is one approach that keeps all data processing within a defined boundary.

Can n8n connect to any API?

n8n supports HTTP request nodes that can connect to any REST API \u2014 which means any service with a documented API can be integrated. Beyond that, n8n has over 400 pre-built integrations for common tools including Slack, Google Sheets, Gmail, Notion, HubSpot, and OpenAI. Custom integrations require basic understanding of API authentication (API keys, OAuth), which is covered in the workshop.

AI Workflows that Work – Build your team’s first n8n automation

Your team is already using AI. But the results are inconsistent — because the prompts are vague, the limits unclear, and nobody really knows where the machine ends and the mistake begins. This workshop changes that. Your team learns how AI and LLMs actually work, sets the right guardrails, and builds a real automation with n8n — together, in one day. One day, on-site or off-site, immediately applicable.

Curious?

Wie entwickle ich eine Kommunikationsstrategie?

AI tools don’t replace thinking. They amplify it — if your team knows how to lead them.

In this workshop, your team builds a shared foundation: how language models work, why prompting is still the key skill in 2026, and where AI fails without guardrails. Then you build. Using n8n, your team connects real tools — email, Slack, Sheets, LLMs — into a workflow that runs on day two. No coding required. Developer thinking optional but welcome.

Strategy and foundations

Prompting — the skill nobody taught your team

Vague prompts produce vague output. Your team learns to write prompts that are precise, repeatable, and verifiable — not just for one tool, but as a transferable skill across every AI system they’ll ever use. Garbage in, garbage out. This section fixes the garbage.

How LLMs actually work — and where they fail

Language models don’t think. They predict. Understanding that distinction changes how your team uses them. We cover what LLMs are good at, where they confidently produce nonsense, and how to recognize the difference. Teams that understand the machine brief it better — and catch mistakes before they ship.

Guardrails — teaching the AI to say „I don’t know“

An AI that answers everything is a liability. We show your team how to set guardrails: constraints that keep the model on task, reduce hallucination, and make output trustworthy enough to act on. A well-guarded workflow beats a fast one every time.

Data sovereignty — what belongs in a tool and what doesn’t

Not every piece of information should go into an external AI system. Your team leaves with a clear framework for data decisions: what’s safe to process, what needs a private model, and how to handle sensitive content responsibly.

Ready to automate the right way?

One day. Your team. Immediately applicable.

REQUEST the n8n workflow workshop

Build it with n8n

Your first n8n workflow — email, Slack, Sheets, LLM

Agentic workflows — when the AI makes decisions

APIs — what they are and why your team should care

Just automate it

Learn how to create n8n automations

Contact us

connect@iniqee.com

+49 30 62931078

Lets Talk

n8n Workshop — Knowledge & Background

What is n8n and how is it different from Zapier or Make?

n8n is an open-source workflow automation tool. Unlike Zapier or Make, n8n can be self-hosted — which means your data stays on your infrastructure, not a third-party server. It supports custom code, API connections, and LLM integrations out of the box. For teams with data sovereignty requirements, self-hosted n8n is often the only viable option.
What is an agentic workflow?

An agentic workflow is an automation where an AI model doesn’t just process input — it evaluates conditions and decides what happens next. For example: an email comes in, an LLM classifies the intent, and depending on the result, a different downstream action is triggered. Agentic workflows require clear guardrails, because the AI is making decisions, not just transforming text.
What is prompt engineering and why does it matter for automation?

Prompt engineering is the practice of structuring input to a language model to produce reliable, predictable output. In automation contexts, prompts are not one-off experiments — they run at scale, on every trigger. A poorly written prompt that fails 10% of the time is a production problem. Good prompt engineering reduces failure rates and makes LLM output trustworthy enough to act on automatically.

What are guardrails in LLM-based workflows?

Guardrails are constraints applied to a language model’s behavior: instructions that tell the model what to do when it doesn’t know something, how to format output, and what topics or actions are out of scope. In production workflows, guardrails prevent hallucination from propagating downstream — into a database, a Slack message, or a customer-facing response.
What is data sovereignty and why does it matter for AI workflows?

Data sovereignty refers to the principle that data is subject to the laws and governance frameworks of the country in which it is stored or processed. For teams in the EU, this means understanding which AI services process data outside GDPR jurisdiction — and structuring workflows accordingly. Using a self-hosted n8n instance with a locally deployed LLM is one approach that keeps all data processing within a defined boundary.
Can n8n connect to any API?

n8n supports HTTP request nodes that can connect to any REST API — which means any service with a documented API can be integrated. Beyond that, n8n has over 400 pre-built integrations for common tools including Slack, Google Sheets, Gmail, Notion, HubSpot, and OpenAI. Custom integrations require basic understanding of API authentication (API keys, OAuth), which is covered in the workshop.