Artificial Intelligence directly integrated into Business Processes

Artificial intelligence delivers real value to companies above all when it does not operate in isolation from existing systems, but can directly access relevant data, applications and processes.

That is exactly the approach we are taking with Cluu.

We do not simply want to offer yet another chatbot. Instead, AI should provide support exactly where it makes a noticeable difference in day-to-day work: finding information, completing tasks, automating recurring workflows and working with complex enterprise systems.

This is how AI in Cluu is gradually evolving into a new interaction and automation layer within the platform.
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From a Traditional Chatbot to an Intelligent Assistant

Many AI applications follow a similar pattern: a user asks a question and a large language model provides an answer.

This is useful for many use cases. In business processes, however, simply providing an answer is often not enough.

Someone asking which tasks in a project are still open may then want to change a status, create a new task or trigger a process directly. This is where the Cluu Assistant comes in.

The Assistant works with the context that is already available in Cluu. Using natural language, users can retrieve information, access functions and initiate processes.

This also changes the way people interact with business software.

Instead of clicking through numerous menus, applications and input forms, users can simply describe what they want to achieve. The platform can then take over part of the interaction required to get there.
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Describe Goals Instead of Searching for Functions

Enterprise software can be extremely powerful and still feel complicated. As the range of functionality grows, it becomes increasingly important to ensure that users can actually access the available capabilities easily.

AI provides an additional way to interact with software.

For example, an employee might ask:

“Which tasks in my project are currently still open?”

Or:

“Create anew issue for this problem and assign it to the responsible team.”

Natural language thus becomes an additional user interface. This does not make traditional interfaces obsolete. Tables, dashboards, forms and specialized applications will continue to be the fastest and most effective option for many tasks. AI simply creates another way to access the system: users no longer need to know exactly where a particular function is located – they only need to be able to describe what they want to achieve.
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AI in the Right Business Context

On its own, a language model knows nothing about an organization, its projects, workflows or data structures.

That is why integration into the platform is so important.

Cluu brings existing enterprise systems and data sources together through a shared data and application layer. Our apps use this context – and AI functions can use it as well.

This is one of the key differences compared with a standalone chatbot.

For example, AI can use information from projects, documents or other applications, provided that the respective user has the necessary access rights.

A general-purpose language model therefore becomes an assistant capable ofworking within a specific business context.
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The Assistant Inherits the User’s Permissions

When it comes to AI in an enterprise environment, the capabilities of the model are not the only thing that matters.

At least equally important is the question:
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Which information is the AI actually allowed to access?
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For this reason, the Cluu Assistant operates within the existing permission model.

The Assistant does not automatically gain access to all data on the platform. Instead, access is determined by the permissions of the individual user.

If an employee is not permitted to view certain information, that information is also unavailable to that employee’s Assistant.

AI therefore integrates into the existing security and authorization structure rather than creating an additional access layer outside the platform.
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Companies Choose Their Own AI Model

Not every company has the same requirements for artificial intelligence.

In some use cases, model performance is the main priority. In others, data protection, infrastructure, costs or regulatory requirements play a more important role.

That is why we take an open approach with Cluu.

Depending on the configuration, different AI models can be integrated. These can include models accessed through ChatGPT or Azure OpenAI, as well as other solutions.

This allows companies to adapt their AI strategy to their own technical and organizational requirements.

We consider this flexibility particularly important because the market for AI models is evolving rapidly. In the long term, an enterprise platform should therefore not be tied to a single model or provider.
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Building Custom Assistants with Cluu Assistant Studio

The same assistant is not necessarily the best choice for every task.

After all, project managers work with different information and processes than developers,testers or quality management employees.

The CluuAssistant Studio therefore makes it possible to create specialized assistants and configure them for specific purposes.

System instructions, functions and the available context can all be defined. Existing elements from Cluu can be reused, and assistants can be connected directly to the platform’s data model.

This makes it possible to create different AI assistants, for example:

Onesupports project management.

Anotherhelps create technical documentation.

A thirdsupports review or approval processes.

Instead of relying on one universal assistant for every conceivable task, companies cantailor AI specifically to individual processes.
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AI Is Becoming an Integral Part of Our Apps

AI in Cluu is not limited to a central assistant.

We are increasingly integrating intelligent functions directly where they provide real value within a specific workflow.

One example is our document management solution. Here, AI can help users find relevant information more quickly across different documents and content.

Over time, large organizations accumulate a vast amount of knowledge. The real problem is often not a lack of information, but the difficulty of finding it again exactly when it is needed.

AI-powered search can help reduce these information barriers.

AI can also support multilingual content. In translation management, untranslated content can be identified and corresponding tasks can be created. Parts of this workflow can then be handled by AI assistants.

Custom terminology and translation packages also help ensure that company-specific terms are used consistently.
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Cluu Co-Driver: AI Directly in Vehicle Testing

The Cluu Co-Driver demonstrates just how versatile this approach can be.

During a vehicle test, testers need to record numerous observations, such as noises, abnormalities, environmental conditions or specific events.

Traditionally, this information is documented afterwards – or a second person is responsible for taking notes.

With the Cluu Co-Driver, we take a different approach.

Testers can record their observations by voice while driving. The information can then bestructured and linked to existing test data.

This allows the driver to focus more closely on the actual testing task.

AI therefore does more than simplify desk-based software interaction. It can also be integrated directly into real-world operational workflows.

This creates further opportunities across the entire testing process – from preparation and documentation to result analysis and issue tracking.
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Why the Data Foundation Matters So Much

When people talk about AI, much of the discussion revolves around which language model is being used.

For enterprise applications, however, we believe another question is at least as important:
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What data and process foundation does the AI work with?
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Even a highly capable model can only provide limited value if company information is distributed across numerous data silos or if the Assistant cannot access the processes it needs.

This is where the architecture of Cluu becomes particularly important.

The platform brings data, applications and processes together. On this foundation, AI can act as the link between users and enterprise systems.

The user describes what they want to achieve.

Cluu provides the relevant context.

The Assistant uses the available functions.

The process can then continue within the same platform.

For us, this is a crucial distinction between an AI chatbot and genuine enterprise AI.
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AI Should Make Work Easier – Not Create More Work

We do not want to integrate AI into as many functions as possible simply because it is technically feasible.

For us, AIbecomes valuable when it simplifies a specific task.

That might mean finding the information someone needs more quickly.

It might mean triggering a complex workflow using natural language.

Or AI might reduce documentation effort while an employee remains focused on the task that actually matters.

One point remains essential: business processes require control.

Permissions, traceable actions and clearly defined capabilities are therefore just as important to an AI solution as the language model itself.
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From Traditional Software Interaction to Goal-Oriented Interaction

In the longterm, AI could change a fundamental assumption of traditional enterprise software.

Today, users often first need to understand how a piece of software works before they can use it to achieve a particular goal.

AI can at least partly reverse this relationship.

The user states the goal – and the software helps identify an appropriate way to achieve it.

This is the direction we are pursuing with Cluu.

For us, AI is not a standalone product that sits alongside the platform. Instead, we increasingly see it as an intelligent interaction, search and automation layer across data, apps and processes.

Our goal can be summarized simply:

Enterprise software should adapt more closely to the task a person wants to accomplish – rather than forcing people to adapt to the structure of the software.

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