01
Clients, employees and the advisory board are asking about AI. But the common cloud tools are ruled out by your data protection requirements.
Local AI Solutions (RAG, IDP)
For managing directors and department heads in law firms, medical practices and industry whose document-heavy processes are subject to data protection requirements.
AI can noticeably relieve your document-heavy processes without data ever leaving your premises. We build RAG and document solutions that run on your own infrastructure - GDPR-compliant and traceable.
From daily operations
Sound familiar?
02
Your team answers the same questions again and again from files, folders and old archives. The knowledge exists, it is just not quickly findable.
03
You see potential for AI, but nobody can reliably say which of your processes is actually suitable and what implementation would involve.
What changes for you
Your team finds answers from files, contracts and archives in seconds instead of searching and asking around - with source references to verify.
Your documents stay on your own infrastructure in Germany or the EU. Using AI becomes compatible with your data protection obligations.
You invest only where AI demonstrably delivers. The pilot is measured against quality criteria defined up front, before you scale.
How it works
1
Assess data sources and protection needs
2
Build pilot with measurable quality criteria
3
Incorporate business feedback
4
Establish governance and operations
What you get
- Use-case definition and data flow design
- RAG setup for internal knowledge search
- IDP pipeline for classification and extraction
- Workflow steps with controlled AI support
Technologies
DMSMail SystemsCRMTicketingWorkflow Platforms
Quality standard
Traceable outputs, quality checks and clear human intervention points.
Your questions
Can we use AI without our data leaving the premises?
Yes. The solutions run on your own infrastructure or on servers in Germany and the EU - matched to your protection needs. Which variant fits is settled before the first implementation step begins.
What does getting started cost?
The entry point is the AI potential analysis: a format with a clear scope, at the end of which you know which of your processes are suitable, what an implementation would require and whether it pays off. Only then do you decide about a project - with a written concept, an effort estimate and hourly billing.
How reliable are the answers of an AI solution?
Language models can make mistakes, so we plan for that: answers come with source references for verification, and at critical points a human decides, not the model. We define quality criteria before the pilot and measure the solution against them.
Do our documents have to be extensively prepared first?
Usually not. At the start we review which sources exist in which quality and tell you honestly what is directly usable and where rework pays off. Perfectly structured data is not a prerequisite.
Related case study
Real projectRAGKnowledge managementSelf-hosted.NET
Local RAG assistant for internal policies and technical documentation
Problem:
- Time-intensive searching
- Distributed systems
Solution:
- Self-hosted RAG
- Answers with source references
Outcome:
- Shorter response times
- Faster onboarding
- No cloud dependency
A defined starting point
AI potential analysis
from 1,500 EUR
We review your processes and data landscape, identify the use cases with the biggest leverage and deliver a written assessment with an effort range. Then you decide whether and how to proceed.