How data science can make pandemic predictions better
How moving to a more complete approach based on individual health behaviour, health state and daily habits gave a global pandemic prediction model more accuracy and became a decision-making tool for governments to improve vaccinations in their country.
Engineering Beyond the Hype: How Knowledge Sharing Drives Production-Hardened AI and Data Systems
In modern enterprise data and AI engineering, technology moves faster than governance frameworks can adapt. New open-source models, orchestration platforms, and vector paradigms emerge weekly. For…
Beyond Vector Search: Building and Benchmarking Enterprise GraphRAG Architectures
Retrieval-Augmented Generation (RAG) has become the standard pattern for grounding Large Language Models (LLMs) in enterprise domain knowledge. However, standard RAG architectures—relying primarily…
Engineering On-Premise LLM Infrastructure: Architecture, Benchmarking, and Production Realities
By Pierre Hockers - Data scientist For organizations operating under strict regulatory bounds or high security requirements, cloud-based LLM APIs present an operational bottleneck. When data…
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.
Sovereign AI Infrastructure in Europe: an engineering and architectural evaluation
By Pierre Hockers - Data scientist European enterprises evaluating Generative AI face a complex infrastructure decision. While the pressure to adopt Large Language Models (LLMs) is high, operating…
Une banque belge réduit son évaluation du risque de 14h à 3h
L'augmentation des risques financiers et cybernétiques nécessite de nouveaux modèles évolutifs pilotés par l'IA, qui améliorent l'évaluation des risques, optimisent la gestion actif-passif et détectent la fraude en temps réel.
Mastering Sovereign AI & Local LLMs
Don’t Let Your Data Become a Liability: navigating the evolving landscape of data modeling
This article serves as a practical guide to choosing the right architectural blueprint to bridge the gap between chaotic raw data and structured insights.
Sovereign compute at scale: architecting for the Belgian AI Factory Antenna
The Belgian AI Factory Antenna gives SMEs and startups unprecedented access to EuroHPC supercomputers, but it comes with strict access rules and technical trade-offs. Discover how to architect your AI workflows for the EuroHPC landscape in our latest guide.
Flight safety boosted with AI-powered obstacle detection
A leading helicopter manufacturer sought a cost‑effective way to detect thin obstacles during low‑altitude flights, requiring an accurate vision‑based system trained with diverse real and synthetic data to improve safety in challenging environments.
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.
Quantifying Retrieval Quality in GraphRAG: A Schema-Agnostic Approach
In this paper, we propose a novel schema-agnostic framework for the automated generation of synthetic evaluation datasets from KGs. Unlike previous approaches, our framework establishes a rigorous, deterministic ground truth to specifically quantify the retriever performance across nine distinct query categories, including multi-hop and aggregation tasks.
Bridging the Gap: The "Lab-to-Fab" Protocol for Production-Hardened Data Systems
The primary bottleneck in modern data engineering is not a lack of innovative ideas; it is the friction encountered when transitioning a successful prototype into a production-hardened system. When…
Efficiency through data governance
A transport company struggled with scattered data and legacy systems, slowing collaboration and critical projects. They needed a centralized platform and operating model to unify governance, improve visibility, and streamline secure, business‑aligned data delivery.
Navigating the AI transition in marketing
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.
Turning retail data into actionable insights
Centralizing data on the Google Cloud Platform (GCP), an international retailer is transforming complex analytics into trusted, production-ready insights—empowering business teams to measure what really matters.
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.
Beyond the hype: how NVIDIA GTC Paris 2025 trends are shaping industry
NVIDIA GTC Paris 2025 revealed an unprecedented scale and breadth of innovation, with a clear focus: not on predicting the future of AI, but on demonstrating how existing technologies are being put to work today. Our CTO Sabri Skhiri was on the ground to bring back insights.
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.
Tech insights from GTC Paris 2025
Among the NVIDIA GTC Paris crowd was our CTO Sabri Skhiri, and from quantum computing breakthroughs to the full-stack AI advancements powering industrial digital twins and robotics, there is a lot to share!