Local AI for businesses: Information and resources
Here you will find guides, use cases, and technical background information on the secure use of AI in your company – with local LLMs and your own AI software.
- Identify AI use cases in your company
- Find guides for planning and implementing customized AI software
- Get a technical deep dive on local LLM and RAG
AI is fundamentally changing the world of work. For companies, this means that now is the right time to examine where AI can improve processes and strengthen their competitiveness.
This AI hub brings together practical knowledge about the safe and responsible use of AI in companies. When dealing with sensitive data, the solution is usually to use your own local AI, which guarantees data sovereignty. You will receive a comprehensive collection of information, guidelines, and best practices for using your own AI software and data protection-compliant AI solutions.
Are you still at the beginning of your AI journey?
We help you get started with local and proprietary AI. Read our AI white paper or explore our AI strategy and consulting services. If you’re considering building your own AI solution – for example, to use AI securely with sensitive data or to automate complex processes – we’ll support you from idea to implementation.
AI strategy and consulting
For companies that want to use AI securely and responsibly: We advise you on the use of local language models and work with you to develop customized solutions that run in Germany or on your own infrastructure.
Looking for your path with local AI?
Our resources help you identify the right AI use case for your business. Can AI support or automate key parts of your operations? We’ll find out together.
Applications for local AI in companies
The discussions we have with customers reveal a clear pattern: With local AI, three typical AI use cases emerge that usually bring noticeable benefits quickly:
- Search: How can I find internal knowledge more quickly?
- Document creation: How can I create repeatable documents?
- Content analysis: How can I quickly extract information from PDFs/emails?
Secure AI deployment in SME
AI is already a major topic in small and medium-sized enterprises, but its widespread use is still in its infancy. This article provides guidance and assistance based on our experience in implementing AI projects for small and medium-sized enterprises.
Benefits of your own local AI application
Local LLM: The technology behind the scenes
Using AI securely and in compliance with data protection regulations – with local LLMs: In our blog post, we show you how to build your own AI with a local LLM and RAG. We also provide insight into the development of our proof of concept: makandra AI.
FAQs about local AI in companies
Cloud AI (e.g., public AI) runs on an external provider's infrastructure. Depending on the setup, data is transferred to third parties and processed.
Local AI (private AI), on the other hand, is operated on your own infrastructure or in a dedicated private cloud. This allows you to retain control over data, access, and compliance.
Cloud AI can be useful if:
- only non-sensitive data is processed,
- fast experiments/prototypes are the main focus,
- no deep integrations are required.
Local AI is ideal when:
- sensitive data/internal knowledge is to be used (e.g., contracts, customer data, tickets),
- data protection, IP protection, or compliance are crucial,
- the AI is to be integrated into core processes,
- Auditability and access control are important.
When people hear "local," many think "runs on my laptop." In business, we mean something different:
Local AI means:
The AI is operated in your own infrastructure (on-premise) or in a dedicated private cloud (e.g., in Germany or the EU) – with:
- Access control (roles & rights),
- Logging & traceability,
- Data sovereignty (your data remains in your environment).
Not necessarily – it depends heavily on the use case.
Cloud AI often seems inexpensive as long as you're just testing. In productive applications, costs often rise due to:
- high token/API usage,
- many users,
- repeated requests,
- compliance efforts.
Local AI has higher initial costs (development costs/infrastructure/setup), but can be:
- be more predictable and cheaper in the long term with high usage,
- reduce vendor lock-in,
- reduce security and compliance costs.
Typically not through "training," but through RAG:
- Your content remains in your systems (e.g., DMS, wiki, file server, tickets)
- Relevant documents are found via secure search/indexing
- The AI only receives the context necessary for the answer
Security is not "automatically there" with local AI – but it is fully controllable.
Important building blocks:
- Role/rights concept (who is allowed to do what?)
- SSO/identity management
- Data classification (which data is allowed in?)
- Audit logs & monitoring
- Segregated infrastructure/network segments
- Rate limits & abuse protection
This makes local AI particularly suitable for:
- regulated areas,
- companies with works council/compliance requirements,
- sensitive internal workflows.
Start your AI project with us
Are you ready to start your own AI project? We would be happy to provide you with a customized quote for your company's AI or advise you on your options and our packages.