AI as a strategic tool

How we boost our customers' business with artificial intelligence

In just a few years, artificial intelligence (AI) has moved from experimental technology to a key tool in modern software development. As a software vendor, we have seen how AI can be a driving force in creating even more targeted, value-adding and user-friendly solutions for our customers - especially in areas where complexity, data volume and decision support play a crucial role.

AI as a player in our customers' digital value chain

In practice, our use of AI is not about replacing human decision-making, but about augmenting it. Many organisations have large amounts of unstructured information - in documents, emails, support systems, technical documentation, etc. Here we see AI as an effective tool to:

Structure and contextualise data
Improve searchability and access to knowledge
Automate trivial or repetitive tasks
Support employees in making faster and more informed decisions

Search and language understanding

A new way to activate knowledge

One of the most exciting breakthroughs in recent times is what is known as Retrieval-Augmented Generation (RAG). Without going into technical details, the model is based on a simple but powerful idea: combining traditional information retrieval with modern language models that can understand and convey information in natural language.

In short, we give the AI access to a customer-specific knowledge base - and enable it to formulate answers, suggestions or insights based on both that and its linguistic understanding. This makes it possible to:

Create intelligent assistants that truly know the customer's domain
Give employees access to relevant information without searching through folders and files
Augment existing systems with a new, natural interface - based on language

AI as a component

We see AI as a component, not a product

When we develop AI-enabled solutions for our customers, it is important to emphasise that AI never stands alone. It is a component that integrates into the customer's existing business systems, data sources and workflows. The greatest value comes when AI is tightly coupled to the context - both technical and organisational.

This requires a combination of:

Domain understanding
Technical integration
Change management
Data governance and responsible use

AI in the future

A look ahead: AI becomes a natural part of software

We believe that in the coming years, AI will become as natural a part of software solutions as databases and APIs are today. And as with other technologies, the key is to use AI wisely and focus on real value for the end user.

Our role as a technical partner is not just to implement new technology - but to ensure it is meaningful, responsible and business-supporting. This also applies to AI.

When we call AI an engine or a component, we mean that it cannot stand alone. Rather, it works as an integral part of your overall software solution. With AI, your software becomes capable of handling tasks that would normally require human intervention. For example, understanding free text, finding relevant knowledge, suggesting next steps or adapting to a specific user. In other words, AI takes your software to the next level (or ten).

We see particular value in using AI when your solution needs to:

provide answers to specific questions (customer service, support, internal knowledge sharing)
handle large amounts of text or data (e.g. for summarisation or analysis)
provide individual advice (personalised actionable suggestions, etc.)
engaging in complex cases where context is crucial.

Or other scenarios that we haven't yet imagined.

RAG stands for Retrieval-Augmented Generation. It is a method where a language model is combined with a search function that retrieves content from your own data sources, such as documents, databases or internal IT systems. The RAG model generates answers based on this information and not on everything else it has been trained on.

The main benefits are:

Your users get more accurate and relevant answers.
You'll be able to document with reference to the source, making the answers appear more reliable and allowing users to dig further directly into the source if they need to.
You gain greater control over what the AI "knows" and advises on.