Data Scientist (AI & Analytics)

Since the launch of ChatGPT, an increasing number of organisations have been exploring the question: "How can we apply AI within our organisation?" At this hotel chain as well, employees have already been using AI applications on their own initiative and recognise the potential to scale their use further. They sought pragmatic AI applications tailored to their domain and an approach focused on creating business value. The hotel chain engaged with multiple partners and ultimately chose to work with us. Our pragmatic approach was the decisive factor in their decision.

Talent Garden provides masterclasses and training programs to students, engaging with them through various online and offline touchpoints. Online interactions include completed contact forms and information requests, while offline touchpoints involve meetings and calls with Talent Garden’s sales team. Throughout the customer journey, from initial contact to final enrollment, Talent Garden collects extensive data*. With a wealth of raw data at their disposal, they sought to improve their enrollment strategy and the effectiveness of their sales team. To achieve this, they asked us to develop a data model that could better predict the likelihood of a new contact eventually becoming a student.

Financial institutions need to process large amounts of documentation. For this particular institution, an internal team facilitates this by, for example, creating summaries using text analysis and natural language processing (NLP). They make these available to the various business units. To conduct audits more efficiently, they wanted to develop a question-and-answer model to get the right information to them faster. When ChatGPT was launched, they asked us to create a proof of concept.

An essential part of Otis's business operations is the maintenance of their elevators. To time this effectively and proactively inform customers about the status of their elevator, Otis wanted to implement continuous monitoring. They saw great potential in predictive maintenance and remote maintenance.

FrieslandCampina Global facilitates local teams in Europe, Asia, and Africa. They want to gain a better understanding of the market and provide the teams with new insights. The goals are to strengthen their competitive position and to identify new opportunities for expansion.

The outbreak of the corona pandemic in early 2020 has turned the world upside down. In addition to countless infections, hospitalisations and deaths, we also saw an outbreak of violence in many countries. Citizens took to the streets, sometimes violently, to protest against the measures taken, but domestic violence also increased in many places and fear and frustration played a role in racism.

Analysing hundreds or thousands of open answers from surveys, interviews or reviews is time-consuming. To better understand those answers, we group them into themes (for example: ease of use, service, delivery or reliability). At Digital Power, we use Large Language Models (LLMs) to quickly categorise high volumes of open answers. Our team built our own secure, transparent tool that lets us see exactly what happens under the hood. But is AI already advanced enough to replace our human researchers?

Artificial Intelligence (AI) is changing the way organisations operate, from personalised customer experiences to automated or assisted decision making, AI helps your organisation leverage your data. However, navigating through this fast-evolving field can feel overwhelming, with terms like AI, Generative AI (GenAI) & Machine Learning (ML) often causing confusion.

With the ongoing developments in the data and AI industry, the hype around AI agents has no signs of slowing down. Jensen Huang, Nvidia CEO, is a strong proponent of AI agents, envisioning a multi-trillion-dollar opportunity, where agents can perform tasks with a high degree of autonomy and revolutionize how people work and how businesses operate. So, in this article we would like to discuss what exactly AI agents are, what are their main components, how they interact together and the basics on how to build one.

Many organisations are feeling the pressure: AI suddenly seems to be high on the agenda everywhere. New tools are following each other at lightning speed, colleagues are experimenting with ChatGPT, customers are asking smarter questions, and in the media, the word “AI” is unmissable. It feels like a train has taken off, and no one wants to be left behind.

With the increasing availability of cloud services that enable companies to leverage Large Language Models, it becomes relatively easy to setup your own GPT-model. However, one important question needs to be answered before you start building: what are the benefits for my organisation?

The AI Document Explorer (AIDE) is a cloud solution developed by Digital Power that utilises OpenAI's GPT model. It can be deployed to quickly gain insights into company documents. AIDE securely indexes your files, enabling you to ask questions about your own documents. Not only does it provide you with the answers you are looking for, but it also references the locations where these answers are found.

Everywhere at events and online, stories are told about what 'data science' is all about. Definitions are anything but consistent. They go from 'getting something of value out of data' to 'it's basically the same as statistics'. And a Data Scientist is 'a data analyst who lives in Silicon Valley' or 'a socially skilled IT person who does something with data'. But what is it really?