For compliance reasons, we are not allowed to use cloud services for sensitive data.
Our offline AI runs completely locally - your data stays where it is generated.
We want to use AI, but our company works with confidential information.
We implement local LLMs on-premises or in your private cloud - secure, isolated, auditable.
We need AI that works without internet access - for internal systems or critical infrastructures, for example.
Our offline models run independently and are fully functional even without an external connection.
Why you can trust us
Data sovereignty in own infrastructure
Availability for productive AI applications
Availability - even without Internet access
System for secure, local AI processing
Target group
Large companies, public authorities & regulated industries
Markets
Germany, Austria, Switzerland
Technological basis
Local LLM models, container deployment, API frameworks, GPU server, own AI stack
Increasing data protection requirements, sensitive data, limited cloud usage? This is exactly what offline AI was developed for.
The solution enables the operation of powerful large language models in a local or isolated infrastructure - completely without cloud dependency.
Companies retain full data control and still utilise AI with the performance of modern models.
If you want to use artificial intelligence without compromising on security and compliance:
Talk to us about your local AI architecture.
Most AI systems run in the cloud - fast, but with compromises in terms of data protection and control. With Offline AI & Local LLM, your entire AI infrastructure remains in your own network. Data, models and results never leave your company - with full performance and maximum security. Here is an overview:
Local Large Language Models run entirely in your environment - no data transfer to third parties, no cloud storage
The systems work independently of Internet connections and are fully functional even in isolated networks
Updates, maintenance and monitoring are carried out exclusively by your IT department - no external access, no external APIs
The model is trained precisely on your data and remains company-internal - 100 % GDPR-compliant
Full performance thanks to GPU-optimised deployment - directly in your hardware infrastructure
Implementation
We review existing systems, networks and security policies to determine the optimum environment for your Local LLM.
We select the appropriate LLM (e.g. open source, fine-tuned or proprietary) and design the architecture for your deployment.
The solution is installed locally or in your private cloud, customised to your hardware and put into operation.
Your existing applications and data sources are connected to seamlessly embed AI into your processes.
We support you with ongoing operation, regular updates and the optimisation of your model.
Technology used
Docker / Kubernetes (containerised deployment)
NVIDIA CUDA / GPU optimisation
Linux / Windows Server
Microsoft 365 / Teams / Dynamics optional
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Offline AI refers to the operation of large language models (LLMs) in a company's own IT infrastructure without using external cloud services. All data remains entirely within the company and is processed locally. This enables the use of AI even in environments with high data protection, security and control requirements.
A Local LLM offers complete data sovereignty, as no data is transferred to external providers. The AI can be used independently of an internet connection and can be customised to internal requirements. This makes offline AI particularly suitable for companies that prioritise data protection, compliance and control without sacrificing modern AI functionalities.
Offline AI is suitable for organisations with sensitive or particularly sensitive data. These include public authorities, financial institutions and companies from the healthcare sector or regulated industries. Companies with strict internal security guidelines also benefit from the local operation of AI models.
Modern local LLMs can achieve comparable performance to cloud models in many use cases. Especially with GPU-optimised operation and targeted training on the company's own data, high-performance results can be achieved that are suitable for productive AI applications.
