From maintenance-intensive to an AI-driven platform

The challenge

Linde Consult’s work is grounded in sector knowledge and practical application. Much of its data comes from public sources such as company websites, news articles, annual reports, and trade media, supplemented with sources like Dun & Bradstreet and LinkedIn Sales Navigator. That mix is what gives the database its value. Separate signals, combined with supporting facts and context, create a fuller view of an industry, from demand and supply to everything in between.

That was also where the challenge became clear. Keeping a multidimensional database like this current required a great deal of manual work. Changes in management teams, product and service portfolios, mergers and acquisitions, and broader market developments all had to be tracked, checked, and categorized correctly. At this scale, that takes time.

The question for the students was straightforward: what can AI and data science contribute to make existing datasets smarter, more current, and easier to scale? The first phase was deliberately practical. Aart Schalk shared two smaller datasets, centered around one main question: how can we keep the fields in our database filled faster and more accurately with up-to-date information, without compromising on quality?

What the students built

As part of the Master Challenge, the students worked on several prototypes, each addressing a different part of the challenge. They explored the data structure behind the platform, developed a scraper and link checker to retrieve company information automatically, and built an AI agent to support data enrichment and structuring. They also worked on supporting tools for areas such as ESG checks, reporting, and data quality.

The collaboration stayed close throughout the project. Schalk was regularly involved in updates, feedback sessions, and content discussions. That meant the students could work from real questions in the field, while Linde Consult could steer the process using years of sector knowledge.

For Schalk, the value lay in more than the technical output. The students also brought a fresh perspective to the way his team worked. Processes that had become self-evident over time were examined again, helping reveal which parts could be improved, accelerated, or made more consistent.

The result

The collaboration delivered more than a set of working prototypes. For Linde Consult, it showed that the move from manual data management to a more scalable, intelligent model and interactive platform is within reach. The AI applications developed during the project made it concrete how data acquisition, enrichment, and market analysis could be organized more effectively in the future.

That insight quickly led to a next step. Based on the project results, a new company was founded: Venactio (www.venact.io). This new venture focuses on further developing the framework and platform that took shape during the collaboration. One of the students, Titas Janušonis, became a co-shareholder, and other students also remained involved in the next development phase.

Venactio’s ambition goes beyond one sector. Its first focus is the food processing industry, but the underlying model is intended as a framework that can later be applied in other industries as well. Sectors such as pharmaceuticals or agriculture face similar challenges: a large amount of fragmented market information, and a growing need for better visibility into supply chains, product developments, the competitive landscape, and acquisitions.