Workflows that run, not just diagrams.
We build automation with n8n and language models that runs in production and that your team can monitor and adjust.
The situation
Many automation projects don't fail on the idea, they fail on execution: workflows that work in testing but break on real data, have no error handling, or that nobody in the company understands or can maintain.
n8n is an open, self-hostable automation tool with very broad connectivity to existing systems. Combined with language models, it can handle processes that used to be too unstructured for classic automation: sorting email, evaluating documents, creating and reviewing content, multi-step approval processes.
We build these workflows with error handling, logging, and a structure your team understands, instead of a chain of nodes only we can maintain.
Deliverables
Process analysis
Clarification of which steps can be automated and where a human should keep the decision.
Workflow architecture
A structure of several traceable n8n workflows instead of one confusing monolithic workflow.
LLM integration
Connection of language models for the steps that need to understand, classify, or generate text.
Error handling and monitoring
Notification on errors and traceability of what a workflow did and when.
Handover and training
Documentation of the workflows and a briefing so your team can adjust them on its own.
Our approach
- 01
Capture the process
We document the existing process with the people and systems involved.
- 02
Design the workflow
We design the workflow structure, including where a language model is used.
- 03
Build and test
We build the workflows in n8n and test them with real, not just sample, data.
- 04
Put into operation
We activate the workflows in production, with error handling and notifications.
- 05
Hand over
We hand over documentation and access and train your team in maintenance and extension.
Typical starting points
| Starting point | Solution |
|---|---|
| A manual, recurring task ties up capacity | An automated workflow with clear error handling |
| Existing automation is unstable or hard to understand | A rebuild with a documented, maintainable structure |
| A task requires understanding text, not just fixed rules | A workflow with an LLM step for classification or text generation |
Solution: An automated workflow with clear error handling
Solution: A rebuild with a documented, maintainable structure
Solution: A workflow with an LLM step for classification or text generation
Outside our scope
We don't build workflows without error handling, and we don't leave without training someone in the company who understands them. For use cases where a language model is too unreliable, we say so and suggest classic rule-based automation instead.
Frequently asked questions
| Ausgangslage | Umsetzung | Ergebnis | |
|---|---|---|---|
| EdTech company, content production | Content production ran step by step by hand and depended on a few people. | n8n pipeline with LLM steps for drafting, structuring, and formatting, with human sign-off. | The content team works on quality, not on mechanical intermediate steps. |
| EdTech company, customer service | Customer service answered recurring questions manually from scattered knowledge. | AI agent with access to the knowledge base, with clear escalation to humans. | Recurring inquiries run productively through the agent, complex cases stay with the team. |
| EdTech company, PE-financed | PE-financed EdTech company, technical picture unclear before the investment. | Tech DD, greenfield architecture, AI agents in production, AI-assisted engineering organization. | The company runs AI in day-to-day operations, not just in pilot projects. |
EdTech company, content production
Ausgangslage
Content production ran step by step by hand and depended on a few people.
Umsetzung
n8n pipeline with LLM steps for drafting, structuring, and formatting, with human sign-off.
Ergebnis
The content team works on quality, not on mechanical intermediate steps.
EdTech company, customer service
Ausgangslage
Customer service answered recurring questions manually from scattered knowledge.
Umsetzung
AI agent with access to the knowledge base, with clear escalation to humans.
Ergebnis
Recurring inquiries run productively through the agent, complex cases stay with the team.
Ausgangslage
PE-financed EdTech company, technical picture unclear before the investment.
Umsetzung
Tech DD, greenfield architecture, AI agents in production, AI-assisted engineering organization.
Ergebnis
The company runs AI in day-to-day operations, not just in pilot projects.
Initial call: 30 minutes, concrete.
We look at a concrete process and tell you whether and how it can be automated.