LudoExplorer, a personal project built end to end

In brief

LudoExplorer is a personal project, built entirely from scratch, that turns a public dataset about board games into a live recommendation experience. It is also a concrete demonstration of the four areas I work on for clients: business and data analysis, automation and applied artificial intelligence.

The challenge

Board game enthusiasts have thousands of titles to choose from, but few simple ways to find a game that genuinely fits their preferences, group size, language or willingness to read a lot of rules. Public data exists on the subject, but it is large, messy and multilingual and cannot simply be used as it is.

The approach

Before a single line of recommendation code was written, the real work was in the data. Making sense of a large and inconsistent public export, with games described in several languages, inconsistent categories, missing fields and duplicates, and deciding what was actually worth keeping. Building a classification that reflects how people really search for a game, not simply how the raw data happened to be labelled. And defining what a good recommendation actually means: a combination of similarity, popularity and fit with player count, complexity and language.

Once that foundation was in place, the data was enriched. Short, purpose-written descriptions replaced the often dry and inconsistent source text, in all three languages of the site.

The automation layer keeps the site running without manual intervention, with updates, backups and health monitoring on a production server.

Artificial intelligence is used where it genuinely adds value: a personalised recommendation engine, more flexible interpretation of search queries, and a personality quiz that gives people a starting point without requiring them to already know the technical vocabulary of the hobby.

The result

A public website with search, filters, detailed game pages, personalised recommendations, a tool for comparing two games and a personality quiz. Full support in French, Dutch and English for search, recommendations and content, not just labels. An internal dashboard tracking real usage, so decisions about what to develop next can be based on facts. A production deployment that runs unattended, with automatic updates, backups and health monitoring.

What this shows

LudoExplorer is small enough to explain on a single page, yet it touches every layer of my work. Understanding the problem and the data before building anything. Cleaning and enriching raw data until it becomes genuinely usable. Making a large catalogue easy to explore, shaped around real behaviour. Automating what needs to keep running on its own. Using AI where it solves a real problem, rather than using it just to tick a box.