GraphRAG: Unlocking Enterprise Knowledge with Knowledge Graphs
Standard RAG struggles with complex enterprise queries. Discover how GraphRAG uses Knowledge Graphs, ontology-driven pipelines, and AI agents for smarter retrieval.
Who judges the AI? Fine-tuning Mistral 7B as a specialized evaluator
As organizations scale their Generative AI from prototypes to production, verifying output quality has become the next major cost bottleneck. Do you really need a 'frontier' LLM to judge your AI? We investigated whether a smaller, specialized model could do the job.
Pioneering the future of aerial intelligence through advanced 3D digital twins
How do we inspect critical infrastructure safely and efficiently? To tackle this, dive into the combination of aerial platforms with advanced AI to create photorealistic, queryable 3D Digital Twins.
Data driven- IT operations in banking
A Belgian bank sought to become data‑driven by unifying fragmented IT data, automating reporting, and building secure pipelines to deliver reliable KPIs, faster decisions, and GDPR‑compliant insights across its IT operations.
Smarter dispatch of technicians
A telecom provider set out to improve service efficiency by predicting the ideal technician for each job, reducing costly misassignments while boosting customer satisfaction through smarter, data‑driven dispatching.
IEEE Big Data 2025: the shift from scale to smart
IEEE Big Data 2025 signals a shift to secure, hybrid intelligence. CTO Sabri Skhiri unpacks the engineering reality from the conference: the practical shift to embeddings, the real need for security layers, and the limitations of AI agents in production.
Evaluation of GraphRAG Strategies for Efficient Information Retrieval
Traditional RAG systems struggle to capture relationships and cross-references between different sources unless explicitly mentioned. This challenge is common in real-world scenarios, where information is often distributed and interlinked, making graphs a more effective representation. Our work provides a technical contribution through a comparative evaluation of retrieval strategies within GraphRAG.
Flight Load Factor Predictions based on Analysis of Ticket Prices and other Factors
The ability to forecast traffic and to size the operation accordingly is a determining factor, for airports. However, to realise its full potential, it needs to be considered as part of a holistic approach, closely linked to airport planning and operations. To ensure airport resources are used efficiently, accurate information about passenger numbers and their effects on the operation is essential. Therefore, this study explores machine learning capabilities enabling predictions of aircraft load factors.
Investigating a Feature Unlearning Bias Mitigation Technique for Cancer-type Bias in AutoPet Dataset
We proposed a feature unlearning technique to reduce cancer-type bias, which improved segmentation accuracy while promoting fairness across sub-groups, even with limited data.
Muppet: A Modular and Constructive Decomposition for Perturbation-based Explanation Methods
The topic of explainable AI has recently received attention driven by a growing awareness of the need for transparent and accountable AI. In this paper, we propose a novel methodology to decompose any state-of-the-art perturbation-based explainability approach into four blocks. In addition, we provide Muppet: an open-source Python library for explainable AI.
The LLM coding revolution needs a rulebook
Teams ship AI-generated code faster than ever — and accumulate debt just as fast. The problem isn't the tools. It's the missing process around them.
Air traffic control agency enables up to 4 more aircraft landings per hour
AI models predict aircraft behavior and optimize capacity, enabling safer, more accurate separation and more efficient runway and flight‑path management.
From data governance to GenAI: strategic insights from Warsaw Tech Summit 2025
This year, one of the biggest technological conferences in Central Europe changed its name to Data & AI Warsaw Tech Summit to reflect the latest technological advancements. Our CTO Sabri Skhiri was on the ground to bring back insights.
Development & Evaluation of Automated Tumour Monitoring by Image Registration Based on 3D (PET/CT) Images
Tumor tracking in PET/CT is essential for monitoring cancer progression and guiding treatment strategies. Traditionally, nuclear physicians manually track tumors, focusing on the five largest ones (PERCIST criteria), which is both time-consuming and imprecise. Automated tumor tracking can allow matching of the numerous metastatic lesions across scans, enhancing tumor change monitoring.
Tech insights from Data & AI Tech Summit Warsaw 2025
11 editions later, one of the biggest technological conferences in Central Europe changed its name to reflect the latest technological advancements. Our CTO, Sabri Skhiri, was present to gather the insights. Here’s a rundown of the key trends, keynotes and talks that took place.
Engineering trustworthy AI in production: your path to traceability and ROI
Four expert speakers from Euranova and Collibra explore how to build ethical and transparent AI systems that drive ROI. Watch the replay.
Unlocking the power of unsupervised learning with interpretable graph embeddings
Unsupervised learning offers immense potential, but deciphering the results is often a challenge. Discover INGENIOUS, a groundbreaking framework from Euranova that generates interpretable graph embeddings that reveal the 'why' behind complex data patterns, empowering organizations to make informed decisions with confidence.
Optimizing truck planning with AI
A major car manufacturer set out to overhaul its spare-parts logistics by shifting from a push to a pull model, aiming to automate complex truck planning and boost speed and accuracy across Europe.
Enhancing productivity and security with computer vision
A leading Belgian food retailer sought to ease warehouse congestion by using computer vision to guide forklift drivers in real time, reducing picker‑driver interference to boost productivity and safety.
A Belgian bank cut risk assessment time from 14h to 3h
Rising financial and cyber risks drive the need for AI‑driven, scalable models that enhance risk assessment, optimize balance sheets, and detect fraud in real time beyond the limits of manual processes.
Edge AI engineering: Euranova’s partnership with STMicroelectronics
This partnership focuses on the technical integration of machine learning models into constrained embedded environments. We provide the architectural rigor required to deploy low-latency, high-throughput AI directly onto STMicroelectronics silicon.
How sales receipt data contributed to review marketing strategy
A sales receipt contain a wealth of primary data on shopping habits, product, and retailer information. Find out how we used this marketing gold mine to support a hyper market in its marketing efforts.
Euranova. Implémenter des solutions technologiques qui n’existent pas encore.
Comment savoir quelles technologies utiliser, implémenter, voire créer de toute pièce afin de répondre aux besoins très spécifiques, voire uniques de certaines entreprises?
Automotive supplier explores unprecedented energy consumption prediction
Can an EV navigation system predict energy use with razor‑sharp accuracy—and turn every route into the smartest, fastest, most efficient drive yet? Answer is yes.