HilmarCorp has reached a milestone: our R&D has matured, and our models — now more complex and demanding — require training capacity that matches the production standards we aim for. This is the purpose of the grant application submitted to Bpifrance, currently under review: to secure the computing resources needed to train these new models properly, strengthen our operational foundation, and transition from lab to market without compromising quality, security, or compliance. At this stage, it remains only an application; no decision has been made.
The need is clear and documented: to stabilize deeper and better-regularized models, training cycles must accelerate, scenario sets must expand, and out-of-sample validation iterations must multiply. Without dedicated high-performance machines, training becomes too slow, calibration insufficient, and robustness difficult to demonstrate at the level expected by professional counterparties. The requested support would provide a permanent computing base, increase experimentation throughput, and strengthen quality controls accompanying every model update.
This scale-up is not about “doing more,” but about doing better: shortening the time from hypothesis to result, improving reproducibility, refining risk control, and hardening end-to-end traceability. It also involves strengthening our secure data environment and observability processes necessary for industrialization (detailed logging, continuous non-regression testing, supervision). In line with our trade secret policy, we do not disclose internal architecture; the essence lies in the method: scientific rigor, engineering discipline, and compliance by design.
The go-to-market plan is staged. We first extend controlled evaluations and intensive paper-trading to lock in stability, then open supervised pilots with selected partners, before cautiously expanding commercialization. Each milestone rests on objective criteria of robustness, operational continuity, and service quality. The requested hardware investment is what allows these steps to unfold without undue delay or dilution of our standards.
Beyond HilmarCorp, the stakes are sovereignty and credibility: anchoring in France (and Europe) critical computing capacity, operational security, and data governance, to deliver an AI-first asset management offer that is performant, explainable, and auditable. This framework enables trust with professional clients and supervisory authorities, positioning a technology born in R&D as a product ready for use, measurable and exportable.
We recognize what public support entails: transparency in fund use, verifiable outcomes, and reporting discipline that meets expectations. If the grant is awarded, we will publish milestones reached and significant operational learnings, while preserving the confidentiality of our intangible assets. If not, our trajectory remains the same in substance; it would simply progress at a slower pace.
In summary, the request to Bpifrance pursues a precise goal: equipping HilmarCorp with the computing capacity required to train and validate increasingly complex models, turning a strong scientific proof into a reliable, compliant, and durable commercial offering. It is a logical, deliberate step that extends our internal standards and prepares the controlled opening of our technology to the market.