FenxLabs

FenxLabs is a hands-on applied AI lab and infrastructure firm headquartered in Amsterdam. We create, roll out, and train the platforms that organizations rely on to operate AI in live, production environments—under the rules and preferences they choose. At the heart of what we do is the ARCHIMEDES platform: an enterprise-ready infrastructure suite for teams that need AI behavior that is deterministic, reviewable, and sovereign. ARCHIMEDES brings together ARC, a configuration-first AI router built for true self-service, designed to stay model-agnostic while sending requests to virtually any model—no matter the source system—whether it’s classic ML pipelines, statistical methods, or other model types, all through one consistent interface; HIM, the Hybrid Infrastructure Mesh; and EDES, the Ephemeral Distributed Execution System. Tailored ARCHIMEDES Implementations We set up and deploy a licensed version of ARCHIMEDES inside your setup—cloud, on-premises, or a combination—so it aligns with your regulatory obligations, your mix of models, and your current architecture. You also receive a complete engineering handover so your teams can operate with confidence. There’s no continuing requirement for us to stay involved just to keep it running. Custom Model Development and Training We architect, train, and deliver models built for your use cases using your proprietary data. Whether you need classifiers, forecasting solutions, or upgraded fine-tuning for language models, we manage the entire workflow end to end: preparing the datasets, training, validating and assessing performance, and optimizing inference. Model delivery, computing, and data stewardship are planned according to your constraints. Once the model is finished, it belongs to you outright. We collaborate with a focused group of clients. Our approach is technically rigorous, commercially straightforward, and centered on long-term ownership rather than ongoing dependence. FenxLabs is led by founders and created by professionals who have operated AI infrastructure at scale.