Agents with access to your knowledge, not chatbots.
We build AI Agents and automated workflows that access real systems and data, for customer service, content, and internal processes.
The situation
A chatbot that only accesses a language model answers general questions and fails on specific ones. An agent that can access your knowledge base, your CRM, or your ticketing system answers the questions that actually reach you, and can trigger actions, not just generate text: open a case, check an order, draft an email.
The difference between a demo and a production agent is rarely the language model. It's the connection to real systems, how errors and edge cases are handled, and the question of when the agent escalates instead of guessing. That's exactly where we focus: integration, error handling, approval logic, and monitoring are the real effort.
We use n8n for workflow orchestration and LLM APIs for language processing, but we choose the technology based on the use case. Sometimes a lean custom build is the better choice over an automation platform.
Deliverables
Use case selection
An assessment of which tasks are suited to an agent, based on data quality, risk, and expected relief.
Connection to real systems
Access to knowledge base, CRM, ticketing system, or ERP, instead of an isolated language model without context.
Error and escalation logic
Clear rules for when the agent acts on its own, when it asks a follow-up question, and when it hands off to a human.
Production-ready workflow
A running system with monitoring, logging, and defined responsibilities, not a prototype that disappears after the demo.
Team enablement
Your team can observe the workflow, adjust it, and extend it with further steps.
Our approach
- 01
Sharpen the use case
We narrow down the use case: which requests, which systems, what risk if something goes wrong.
- 02
Check the data situation
We check whether the necessary information is accessible and current enough for the agent to answer reliably.
- 03
Build and test a prototype
We build a first workflow and test it against real, anonymized requests from your own history.
- 04
Bring it into production
We switch the agent on step by step, with monitoring and a defined escalation path to humans.
- 05
Expand
We extend the workflow with further use cases once the first one runs stably.
Typical starting points
| Starting point | Our approach |
|---|---|
| A chatbot only answers general questions, not ones about your own products or processes. | An agent with access to your own knowledge base and the relevant systems. |
| Recurring manual steps in content production or back office tie up capacity. | An n8n workflow that automates the steps and only escalates when uncertain. |
| A pilot project went well in the demo but never made it into real operation. | Error handling, monitoring, and a clear owner for production operation. |
Our approach: An agent with access to your own knowledge base and the relevant systems.
Our approach: An n8n workflow that automates the steps and only escalates when uncertain.
Our approach: Error handling, monitoring, and a clear owner for production operation.
Outside our scope
We don't build an agent that works without access to real data, and none that guesses instead of escalating when uncertain. We don't take on use cases where an agent error carries a risk we haven't jointly assessed beforehand. And we don't ship automation without monitoring. A workflow nobody watches isn't a production system.
Frequently asked questions
| Ausgangslage | Umsetzung | Ergebnis | |
|---|---|---|---|
| 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, 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, 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, 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.
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.
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 check against a real use case from your operation whether and how an agent pays off.