Local AI: Implementing Your Own Enterprise AI
Custom enterprise AI, developed by experts in business-critical software and AI applications.
- Assessment of custom AI use cases
- Development of your custom AI solution
- High-quality software and the highest security standards
- Secure, highly available operation in Germany
Why develop your own AI?
Many companies are already using ChatGPT or working with tools like Copilot. However, public AI services aren’t always the right fit for every business challenge.
If data protection and data security requirements are more stringent, or if your use case cannot be addressed with existing AI tools, we develop a custom AI solution for your company.
Challenges of Standard AI
- Caution regarding sensitive company data
AI prompts are processed in external clouds, often outside the scope of the GDPR. - Standard AI cannot handle all processes
Your business is unique, and in some cases, Copilot and other standard AI programs reach their limits. - AI for Your Core Business
Standard solutions carry risks, especially when it comes to critical processes in your core business where downtime is unacceptable.
What are the advantages of local AI?
- Full control over data & costs
Your local AI runs on your infrastructure or in a GDPR-compliant data center in Germany. - Harness the potential of your data with peace of mind
Use all your company data easily and with confidence: Leverage the power of AI to achieve precise results tailored to your specific needs. - Your own AI: Independent & secure
Use LLMs for internal applications, such as knowledge management, automation, or chatbots. No dependence on external providers: your AI is your property.
Use Cases for Your Local AI
Internal knowledge search with RAG, source citations, and wiki/documentation assistant:
- Employees ask questions in natural language and receive relevant answers directly from internal documents, wikis, or systems
- Relevant sources are provided, ensuring that answers remain traceable and verifiable
- Knowledge from different systems becomes centrally accessible – without manual searching
Extraction from supplier PDFs, transfer to SAP, less manual work:
- Content from PDFs with varying structures is automatically recognized, extracted, and converted into structured data
- The results can be imported directly into existing systems such as ERP or SAP
- Manual data entry and error-prone processes are largely eliminated
Automatically create Word documents from source documents:
- Information from various source documents is automatically merged and entered into existing templates
- Layouts and existing document structures are fully preserved
- Recurring documents are created much faster and with greater consistency
Draft responses based on existing knowledge, local text generation for sensitive content:
- Frequently asked questions are automatically answered or prepared based on existing content and past communications
- The AI creates well-founded draft responses, which are reviewed and approved by subject matter experts
- Departments save time while retaining control over the content
Not every effective use of AI follows a standard pattern – many solutions are developed directly from a company’s specific processes. Working with you, we identify the areas where AI creates real value and, building on that, develop a solution that is precisely tailored to your requirements, data, and existing systems.
makandra Magazine: Our successful AI projects
At makandra, we specialize in tailored solutions that are efficiently and effectively designed to meet the specific processes and needs of our clients. Our clients benefit from our technical expertise: we are passionate technologists who understand our craft down to the last detail. In our AI magazine, we showcase AI use cases that we have successfully implemented.
When developing software, makandra doesn’t just follow the rules; they are always willing to go the extra mile. Automated testing, UX enhancements, and suggestions for improvement are rarely found on this scale.
Local AI: Flexible and Future-Proof
With a local AI solution, you remain flexible and independent at all times. Your AI never becomes obsolete – you can update the language model at any time or switch to a more powerful model as needed. This allows you to enjoy the benefits of your own AI today while ensuring the ability to seamlessly integrate future innovations.
Your AI can be further developed at any time, ensuring it remains state-of-the-art well into the future.
AI Starter Kits
Developing your own AI doesn't have to be expensive: Start with a small pilot project or an idea generation workshop. You'll discover concrete ways to implement it, even on a small budget.
Lookin to develop initial ideas for AI applications in your company or further refine and clarify a specific AI idea.
Process:
- Preliminary discussions and preparation for your AI ideas
- 1-day workshop: we analyze and refine your AI use cases
- Final meeting with presentation of the results
Results:
- Detailed concept for specific AI use cases
- Estimates of costs and resource requirements
- Recommended next steps
Whether you want to evaluate new business models using AI, optimize processes through automated solutions, or turn a specific AI idea into an initial prototype.
Process:
- Preliminary discussions regarding the specific requirements for the prototype
- Development of the prototype/feedback round
- Refining the prototype into a final MVP
Results:
- Implemented and functional prototype of an AI application
We develop your local AI
With custom development, costs are determined based on the range of features you require. Your AI is built on the makandra AI platform. The foundation for your AI is already in place, which reduces development costs. Please feel free to contact us if you have any questions about budgeting or our packages.
makandra for your business AI
Off-the-shelf solutions like Copilot or ChatGPT are quick to deploy, but they reach their limits when it comes to business-critical requirements. If AI is becoming part of your core business or is intended to solve a complex problem, you need a solution that integrates seamlessly with your systems, processes, and security requirements.
FAQ on Local AI
On-premises AI is an AI solution that runs on a company’s own infrastructure or in a dedicated, GDPR-compliant data center. Unlike cloud services such as ChatGPT or Microsoft Copilot, no company data is transferred to large, international AI providers. The company retains full control over the data, the model, and operations.
Having your own in-house AI is worthwhile if you work with sensitive or confidential data (e.g., patient data, financial data, trade secrets), if off-the-shelf solutions cannot accommodate your specific processes, if AI is becoming part of your core business, or if independence from external providers and their pricing is important to you. For companies in regulated industries (healthcare, legal, finance), having your own on-premises AI is often the only option that complies with data protection regulations.
With cloud-based AI services, your inputs (prompts) are processed on servers belonging to third-party providers – often outside the EU’s scope of the GDPR. With on-premises AI, your data never leaves your own data center or infrastructure. There is no reliance on third-party providers, no unintended use of your data for model training, and no uncontrolled data transfers. This is particularly relevant when employees are working with confidential customer, financial, or production data.
On-premises AI is the better choice when standard cloud solutions reach their limits. This is typically the case in three situations:
The use case is too complex or too specific. Copilot and ChatGPT are designed for generic tasks. If your processes require specific company knowledge, proprietary data sources, or deep system integration, standard tools quickly reach their limits – a custom AI knows your business.
It involves business-critical processes. When errors or outages are not an option – such as in production, customer service, or regulated sectors like healthcare or finance – you need an AI that is reliable, controllable, and fully tailored to your requirements.
You don’t want to be dependent on external providers. With cloud AI tools, you’re bound by a third-party provider’s pricing policies, availability, and data protection terms. On-premises AI belongs to you – you decide which model runs, when it’s updated, and where the data stays.
For companies with heightened data protection or customization requirements, on-premises AI solutions are the relevant alternative to Microsoft Copilot. Instead of relying on a standardized cloud solution, companies can run their own Large Language Model (LLM) on their own infrastructure and tailor it precisely to their own business processes, data, and systems. makandra develops such customized AI solutions and integrates them into existing CRM, ERP, or DMS systems.
The cost depends on the desired scope of functionality. As an entry point, makandra offers an AI workshop starting at €5,000, during which specific use cases are analyzed and an implementation plan is developed. A first functional prototype (AI MVP) can be developed starting at €8,000. For a complete custom development, costs are calculated individually based on the scope of functionality. A free initial consultation helps you find the right starting point.
Typical applications include: making corporate knowledge easier to find (internal knowledge databases, document search), automatic analysis of documents and PDFs, intelligent completion of templates and forms, streamlining support and internal communication, and automating repetitive processes. The AI is trained or configured to handle the specific data, language, and workflows of each company.
Yes. A custom-developed enterprise AI can be seamlessly integrated into existing IT systems – including CRM systems, ERP solutions, document management systems (DMS), and internal knowledge databases. This integration ensures that the AI can access relevant business data and be used within employees’ existing workflows.
