AI projects – how do they really work and what does it take?
Artificial intelligence (AI) has gained widespread attention through applications such as ChatGPT. Large language models are useful, but quickly reach their limits when specific company requirements need to be taken into account. A language model alone is not enough to create sustainable and scalable solutions, as it is not tailored to a company’s individual processes or industry-specific requirements.
So what does it take for AI to really make a difference? The answer lies in a well thought-out combination of technology, a clear structure and consideration of a company’s specific needs. This article looks at the key fundamentals – from real-life use cases to the requirements that make AI projects successful.
Why the big language models often reach their limits
Large Language Models (LLMs), such as ChatGPT, LLaMA, Claude and co. offer many useful approaches, but they often do not comply with the strict requirements of the GDPR or the EU AI Act. This creates data protection problems and legal uncertainties.
Another key point is integration into existing systems. Companies work with ERP or CRM platforms that are essential for their operational processes. Without a smooth connection to such systems, data islands are created that reduce efficiency and encourage errors. Linking AI with existing systems is therefore not just a technical detail, but a basic prerequisite for AI to be able to offer added value.
These limitations make it clear that LLMs alone are not enough to meet the complex requirements of modern companies. They make it clear why a specialized solution is needed – a platform like indom.ai that addresses and overcomes precisely these challenges.
How indom.ai supports companies
indom.ai, a product of neuland.ai AG, offers companies a specialized platform that meets all the essential requirements of a modern AI operating system. Through the integration of Industry Competence Models (ICMs), indom.ai combines industry-specific knowledge with state-of-the-art technology. The platform delivers precise, legally compliant results and guarantees the highest data security standards.
With indom.ai, companies benefit from a central infrastructure that serves management, IT and end users alike. It enables the efficient integration of AI into existing systems, reduces sources of error and increases productivity. Thanks to the platform’s flexibility, it can be operated on-premise, in the cloud or in hybrid environments and thus adapts to individual requirements.
The diagram illustrates the challenges in AI projects that arise from the gap between user requirements and the security and control needs of IT management. A central AI architecture helps to close this gap.
The strength of indom.ai lies in bridging the gap between conventional LLMs and the specific needs of companies. It offers not only tools, but also a solid foundation for sustainable success with AI.
The importance of Industry Competence Models (ICMs)
Industry Competence Models (ICMs) are a central component of our AI platform. These industry-specific models deliver precise and reliable results by being tailored to specialist knowledge, standards and regulatory requirements. Compared to generic models, which often achieve an accuracy of just under 50% without specialized customization, ICMs significantly reduce the error rate and achieve accuracies of up to 98%.
This high reliability results from the specific adaptation to the respective industry. In mechanical engineering or the energy industry, where errors are not only expensive but can also be legally problematic, ICMs provide a solid basis for the use of AI.
Insight into AI projects that are already in use
Customized AI at SVT
One example of a successful application is SVT‘s customer specification AI. This company works with extensive documents that include technical drawings, standards and detailed requirements. The manual review process was time-consuming and error-prone: it previously took four engineers four weeks to complete the analysis. By using a specially trained AI, relevant information can now be extracted and structured automatically – at the touch of a button. This allows the engineers to concentrate on other tasks while the AI takes over routine tasks efficiently. This shows very precisely how AI can make everyday work easier.
User interface of the SVT platform for fast analysis and processing of customer specifications with AI projects.
Tender management at madiba
Another example is tender management at madiba. Before the introduction of AI, the process was extremely time-consuming: relevant tenders had to be searched through manually and suitable experts assigned based on individual assessments. This effort could take up to 1.5 months per project, which was not only inefficient but also led to delays.
Following the introduction of an innovative AI-supported system that combines generative AI and web crawling, the process has changed drastically. Relevant tenders are now automatically identified, analyzed and assigned to suitable experts within just 1.5 hours. This system not only works faster, but also more precisely and makes it possible to deploy resources in a targeted manner.
The image shows how madiba used an innovative AI system to reduce the tendering process from 1.5 months to 1.5 hours. The combination of generative AI and web crawling enables relevant tenders to be analyzed efficiently and resources to be deployed in a targeted manner. AI projects
As a result, madiba benefits from a significant increase in efficiency and optimized international collaboration in over 70 countries. AI revolutionizes the entire process, opens up previously untapped opportunities and raises the quality of the results to a new level.
The added value of small automations
In addition to such complex applications, there are also smaller automation projects that make it easier to get started with AI. Examples include travel expense management, email automation and the processing of incoming invoices. Other “simple” but effective use cases include automatic scheduling, creating summaries for meetings or the intelligent sorting of documents in digital archives. The automated processing of customer inquiries by chatbots or the optimization of social media postings are also included.
These small steps often show quick results, build trust in the technology and solve specific everyday problems that many teams have been grappling with for a long time. They make it clear that AI not only supports large projects, but can also make everyday processes more efficient. Especially in areas that are characterized by recurring tasks, AI offers considerable savings potential and creates space for value-adding activities.
The key finding: an integrated AI operating system
Experience from over 70 projects clearly shows that isolated solutions are not sustainable. Companies need a central platform that manages all AI projects in a structured manner and makes them scalable. Such a platform, like indom.ai, makes it possible to integrate and manage not only large projects, but also smaller automation projects in a targeted manner.
A particular highlight is the drag-and-drop editor, which allows teams to design simple applications themselves – without any prior technical knowledge. This means that the above-mentioned use cases, such as the automation of customer inquiries or the optimization of social media postings, can be implemented flexibly and individually. This intuitive handling lowers the barriers to the use of AI and quickly creates trust in the technology.
For AI to develop its full potential, it is crucial to avoid the silo structures that have emerged in many digitalization projects in the past. Establishing a centralized AI architecture ensures transparency, efficiency and long-term competitiveness. Companies that take this step early on not only secure operational advantages, but also create a basis for sustainable innovation – at a time when technological progress is advancing rapidly.
With indom.ai, companies have the opportunity to bundle their AI projects – whether large or small – centrally and adapt them flexibly to their needs at the same time. This prevents chaos, promotes collaboration and ensures that no resources are wasted.