Generative AI


Generative AI – Assistant with Versatile Skills

“Technology to transform your way of working based on the organization’s needs”

At Top Data Science, we have been closely following the development of generative AI. The evolution of large language models (LLMs) has been accelerating for years, but the release of ChatGPT has exploded global interest in these technologies. The easy-to-use conversational user interface has brought the service and its powerful capabilities accessible to everyone with an internet connection.

Our approach to supporting organizations and individuals who want to benefit from these types of generative AI systems follows our service approach. By providing deeper insights into the technology, helping the customer identify the automation potential, and selecting the right AI approach as well as consulting on the data and technology integration, we enable our customers to unlock value from generative AI. The key aspect of our approach is to provide a roadmap to the customer organization that is aligned with the business development priorities and enables moving forward with well-considered practical actions.


ChatGPT is a conversational LLM developed by OpenAI, a US-based artificial intelligence research laboratory. OpenAI has introduced a series of LLMs: GPT-2 (2019), GPT-3 (2020), GPT-3.5 (2022), and GPT-4 (2023). This model version evolution is expected to continue providing new capabilities to ChatGPT users. In ChatGPT the primary user interface is a chatbot that takes in textual input and answers in text format.

The power of ChatGPT relies on the immense range of domains and knowledge that the model contains, which makes the service feasible for a wide range of use cases including:

  • Public chatbots/automated helpdesk
  • Internal knowledge/documentation management
  • Risk analysis and reporting
  • Prototyping different approaches for implementing solutions to specific user problems
  • Data assessment and analysis
  • Semantic search systems, e.g. in legislation or research
  • Code generation and documentation

The potential transformative value of ChatGPT is tremendous as the list above testifies. At the same time organizations that decide to utilize these capabilities should be aware of and consider several important aspects to minimize risks relating to relying on new technology. These include:

  • Possibility of incorrect answers
  • Potential bias in answers
  • Possibility of missing or inadequate sources
  • Data privacy and security

Top Data Science’s flexible and effective service approach will enable your organization to make a quick assessment of the potential value of ChatGPT and to make an informed decision on how to approach generative AI in general.



Deeper understanding of generative AI and ChatGPT in the context of organizations’ domain and business needs


Support for creating a business case analysis and technology utilization roadmap


Practical action plan on how to start driving benefits from generative AI

Generative AI for Images

Another field that is rapidly evolving is the generative AI for generating new, original images that have never been seen before. These algorithms are trained on large datasets of existing images and use deep learning techniques to learn the underlying patterns and features that make up those images. Generative AI for images has numerous applications, such as creating realistic images for virtual and augmented reality and generating high-quality images for advertising and marketing. Some of the well known models in the area include Midjourney, DALL-E, Deep Dream Generator, and Stable Diffusion.

Figure 1. Image options created by Midjourney using a prompt: “Mountainscape in the summertime with pine trees at sunrise, Canon RF 16mm f:2.8 STM Lens, hyperrealistic photography, style of Unsplash and National Geographic”.

Figure 2. Image options created by Midjourney using a prompt: “Design a modern studio apartment living space with natural light and green plants”.

Generative AI integration approaches

The use of generative AI technologies can be divided into two main categories:

  • Chatbots and other applications with end-user interface
  • Integrations to other systems and applications through APIs and plugins

In both cases the evaluation of how to integrate generative AI service to your business should start by defining the objectives for the technology usage and specifying the desired future state of the working processes. At Top Data Science, we have established a simplistic method to document the use cases in such a way that helps the organization both to calculate the potential value of the use of generative AI in focus, as well as to crystalize how the operative process would be executed with the support of new technology.

The next steps in the implementation planning are to cater to the technical integration as well as to plan the data integration in case the organization is planning to use its own data as part of a generative AI-based service. As mentioned above, the integration of ChatGPT is currently possible with the use of APIs or plugins. Such integration can enhance its capabilities and allow for more versatile and customized chatbot interactions. By integrating with the ChatGPT API, users can instruct ChatGPT to send data to external services or applications, either through their own APIs or via the use of plugins. The key value-add in using plugins is that it allows ChatGPT to access up-to-date information, run computations and use third-party services.



Understanding how the generative AI service and capability in focus will transform the business process and work tasks of the organization and individuals


Understanding of the choices and alternatives for integrating the technology to current business systems and applications


Assessment of financial investment required to benefits from new technology