From practice: How AI supports everyday business

Blog post
Data & Cloud Services
Ian Shulman
23
.
02
.
2026
AI in everyday business

How a medium-sized company transformed its information flow with AI

When information becomes like a needle in a haystack

Perhaps you are familiar with this situation: information is scattered throughout the company, documents are difficult to find, and searching for them takes up an unnecessary amount of time. This was exactly the case at a medium-sized company in the building materials manufacturing industry with around 700 employees. When numerous departments—from production to sales to service—rely on up-to-date data on a daily basis, unstructured information flows have a particularly strong impact. The existing documents were stored in a confusing manner, changes were not documented centrally, and relevant information could only be found by employees with considerable effort. This led to delays in internal knowledge transfer, made onboarding new employees more difficult, and had a negative impact on the quality of customer communication. In addition, translations and the maintenance of product information were very time-consuming. External service providers often had to be brought in, which resulted in high costs. The goal was therefore clear: to make processes more efficient, make company knowledge centrally available, and improve both internal and external communication.

Digitized processes with artificial intelligence

The solution lay in the introduction of a modular AI-based platform based on GPT, which was implemented step by step in collaboration with our AI experts.

Expansion stage 0: Creating texts faster and more consistently
It all started with automated text generation for marketing, sales, and service. Whether website content, newsletters, or email formulations—texts are now created much more efficiently while remaining linguistically consistent and professional. This saves time and ensures a uniform public image.

Phase 1: Making company knowledge centrally available
In the next step, internal knowledge was structured, bundled, and made accessible to employees. The effect is particularly evident during onboarding: new colleagues find answers more quickly and teams can access relevant information in a targeted manner—without spending a lot of time searching.

Phase 2: Keeping product data and documents under control
Building on this, access to current product data and technical documents was reorganized. Instead of distributed storage locations, current versions, historical changes, and technical details are now available centrally—transparently and traceably.

Expansion stage 3: Intelligent support for customer communication
With its connection to the ERP system, the platform went one step further: standard inquiries and complaints can be processed with AI support. Responses are based on current data, are consistently formulated, and significantly reduce the manual effort required for service.

Expansion stage 4: Automatic content translation
Finally, automatic content translation was integrated. Information is now available in several languages (FR, NL, EN, ES, CH) – quickly, consistently, and without any additional coordination effort.

Technical implementation with a system

At the heart of the solution are several specialized pilot chatbots developed for different areas of application. They are connected to the website and internal data sources and feature personalized user interfaces tailored to specific tasks. Multi-stage testing procedures ensure that the responses are technically accurate, linguistically appropriate, and consistently formulated. At the same time, the system is continuously being developed: a new, logically structured document storage system, central versioning of technical documents, automated assignment of product data, and a dashboard for a transparent overview of data and history are already in the planning stage. The result is not an isolated solution, but a scalable platform that grows step by step with the company's requirements.

Noticeable changes in everyday business life

The introduction of the AI platform has noticeably changed the way the company works. This can be seen, for example, when an employee needs up-to-date product information to answer a customer inquiry. In the past, they had to search through several folders and systems, but today the AI platform provides the answer within seconds. Such small but significant efficiency gains make all the difference.  

The advantages at a glance:

  • Increased efficiency: Information is immediately available to employees, which shortens response times and speeds up internal processes.
  • Quality improvement: Consistent, up-to-date, and traceable information ensures greater professionalism in customer communications .
  • Cost savings: Automated processes reduce the need for external service providers and relieve the burden on internal resources .
  • Scalability: The modular architecture allows for flexible expansion, enabling new requirements to be implemented quickly.

The combination of process automation, centralized knowledge management, and AI-supported communication ensures that the company not only operates efficiently today, but is also well equipped to meet the challenges of the future.

Our conclusion: How AI delivers real added value

The project impressively demonstrates how medium-sized companies can drive forward their digitalization through the targeted use of AI solutions. Internal processes become more efficient, the quality of customer communication improves, and company knowledge is made available centrally and transparently. Thanks to its modular and scalable architecture, the platform can be expanded flexibly—a practical example of how artificial intelligence can improve everyday work in the long term. Would you also like to benefit from these advantages? Contact our experts and find out which solutions deliver the greatest added value for your company.

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Webinar on demand
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Blog post author

Ian Shulman
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Ian Shulman
Senior Data Scientist
celver AG

Ian Shulman is a Data Scientist at celver with a particular focus on text generation using Large Language Models. He focuses on simplifying complex and cumbersome processes through machine learning approaches and using data science methods to extract new insights from the data.

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