Your AI & Data Science partner

Dedimo wanted to explore how AI could help automatically transcribe therapy sessions between client and therapist and generate reports.

Heerema Marine Contractors operates the world’s largest crane vessels, equipped with many sensors that together generate millions of measurements every day. This sensor data is critical for safer operations, lower emissions, better engineering and well-founded investment decisions.

When the regular Analytics Translator of a service organisation went on maternity leave, the team sought our help to ensure ongoing AI projects ran smoothly. At the same time, the organisation wanted a fresh, external perspective: how was the role being filled, and where could improvements be made?

Volero.nl is a young and fast-growing company that sells rugs through a webshop and a physical store. The company is primarily active in the Netherlands but is growing rapidly across Europe, including Belgium, Germany and Poland. To support this growth, it is essential for Volero to work in a data-driven way.

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.

Vesteda was curious if social listening – monitoring and analysing social media discussions about a brand, competitors, products or hashtags/keywords – could add value to the organisation. To this end, we started a project that consisted of two parts: exploring possibilities for social listening in the Corporate Communication department and applying social listening in an ongoing Data Science project.

RNW Media builds online communities in countries with limited freedoms. In these communities, young people can read and discuss sexual and reproductive health and rights (SRHR) and civil rights. In addition, RNW Media is working on advocacy – putting the interests of young people on the map with governments.

Residential investor Vesteda is working on a new website. If an available rental home has a garden, the location of the garden must be listed on the webpage of that home. This information was not yet available in the database. We were instructed to determine the location of the garden based on the coordinates of the homes.

Fair Wear is a non-profit organisation that aims to improve the working conditions of employees in garment factories. The NGO has collected a lot of documentation about its activities in recent years, for example in the form of reports from a complaint line for factory employees, reports of audits that check whether factories comply with the guidelines, and reports of training for factory employees. This information is stored as typed text, usually in Word or PDF format.

An increasing number of people are traveling by public transportation. Several stations in The Netherlands are being rebuilt or renovated to keep up with the growing number of train passengers. For the rebuilding and layout plans, information was needed on station traffic. NS Stations also wanted to improve transfer safety in collaboration with ProRail.

Exa is an AI company that develops a search engine and API that enable AI systems to intelligently search and analyse the internet. Their technology is used across domains such as finance, coding agents, news, recruitment and consulting, where large volumes of online data are quickly retrieved, structured and summarised for specific use cases.

For professionals in data, analytics, martech and customer experience, Digital Velocity is one of the events where developments become tangible. It brings together practitioners, partners and industry leaders to show how they approach AI, real-time data and customer experience in practice.

Large Language Models (LLMs) have quickly become a standard component in modern applications. Most developers start by integrating models such as OpenAI, Claude or similar providers through APIs. It is fast, convenient and requires very little infrastructure.

Dedimo wanted to explore how AI could help automatically transcribe therapy sessions between client and therapist and generate reports.

Heerema Marine Contractors operates the world’s largest crane vessels, equipped with many sensors that together generate millions of measurements every day. This sensor data is critical for safer operations, lower emissions, better engineering and well-founded investment decisions.

Artificial Intelligence is transforming how you design, build, and maintain digital solutions. From code generation to data pipeline automation, AI has become a trusted companion in technical workflows.

When the regular Analytics Translator of a service organisation went on maternity leave, the team sought our help to ensure ongoing AI projects ran smoothly. At the same time, the organisation wanted a fresh, external perspective: how was the role being filled, and where could improvements be made?

Volero.nl is a young and fast-growing company that sells rugs through a webshop and a physical store. The company is primarily active in the Netherlands but is growing rapidly across Europe, including Belgium, Germany and Poland. To support this growth, it is essential for Volero to work in a data-driven way.

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.

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.

The internet provides a massive amount of interesting social media posts, likes and shares. A wealth of information, especially for organisations wanting to make more impact online. But where do you start? What data will you collect, how will you analyze it and how can you convert insights into concrete action points? To answer these questions, it is important to start with the mission and goals of the organization.

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.

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.

Artificial Intelligence seems capable of everything, and sometimes even better and faster than what we can do ourselves. Analysing qualitative data is a time-consuming task, and as researchers, we are curious if it can be done faster and easier. Does AI offer a solution for this? Our researchers investigated.

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.

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?

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.

At the 'MKB Data Science Top 50', 50 agencies competed for the title 'the fastest-growing SME data science agency in our country'. Even before the event, we heard that we were in the top 3! During the Den Bosch Data Week, Marieke got to pitch our organisation.

Vesteda was curious if social listening – monitoring and analysing social media discussions about a brand, competitors, products or hashtags/keywords – could add value to the organisation. To this end, we started a project that consisted of two parts: exploring possibilities for social listening in the Corporate Communication department and applying social listening in an ongoing Data Science project.

Tuesday, 3 March 2020, was known as Super Tuesday, the day on which several American states vote simultaneously for the Democratic presidential candidate. We use this day as a case for the application of Social Network Analysis. This example is about elections, but you can also apply the same method to a commercial case where you replace the names of the candidates with, for example, different brand names.

The Corona measures are a much discussed topic on Twitter. The crisis team not only fights against Corona's effects on public health, but also tries to maintain legitimacy for the decision to keep certain measures in place among the public. With this practical case we explain how you can make public opinion on Twitter transparent with Social Network Analysis.

If your organisation is active on social media and you want to optimise the online strategy, you need to know what is happening online around you and the impact of your activities. Social Network Analysis can help you with that. We explain what it is, how it works and the purposes it serves.

RNW Media builds online communities in countries with limited freedoms. In these communities, young people can read and discuss sexual and reproductive health and rights (SRHR) and civil rights. In addition, RNW Media is working on advocacy – putting the interests of young people on the map with governments.

More and more organisations are getting started with data science. A logical consequence of this is clearly a growing number of related vacancies. But how do you set up a useful job description for a data scientist – and mostly: how do you actually pick the right one? We're giving you some hints on what to do, and what not.

The introduction of the new privacy law (GDPR) in 2018 has ensured that many organisations put privacy high on the agenda. In this article you can read about the 5 ethical risks of working digitally and using data. We also share a concrete solution: the Responsible Data Framework.

Residential investor Vesteda is working on a new website. If an available rental home has a garden, the location of the garden must be listed on the webpage of that home. This information was not yet available in the database. We were instructed to determine the location of the garden based on the coordinates of the homes.

Organisations wanting to get started with data quickly ask for Data Science solutions. Data Science is often seen as the holy grail of data-driven working. But what does a successful Data Science project actually look like in practice? And how can it serve your organisation? In this series of articles, we take you through all the elements you need to achieve success for your organisation with Data Science.

Fair Wear is a non-profit organisation that aims to improve the working conditions of employees in garment factories. The NGO has collected a lot of documentation about its activities in recent years, for example in the form of reports from a complaint line for factory employees, reports of audits that check whether factories comply with the guidelines, and reports of training for factory employees. This information is stored as typed text, usually in Word or PDF format.

An increasing number of people are traveling by public transportation. Several stations in The Netherlands are being rebuilt or renovated to keep up with the growing number of train passengers. For the rebuilding and layout plans, information was needed on station traffic. NS Stations also wanted to improve transfer safety in collaboration with ProRail.