Xpdeep Information

Enterprises have invested tens of millions in data platforms, test benches and models that still hand every consequential decision back to a human. A black-box model returns a prediction and nothing else, so the system around it compensates: an expert behind every call, conservative limits, redundant sensors, constant alerts. Expert attention, not model accuracy, caps what AI delivers. Xpdeep builds the first deep learning models that can be read from the inside. On every inference, the model returns one integrated decision object: what drove its output, whether it is still inside the domain it was trained on, a minimal feasible intervention under operational constraints, and the structural justification of why that intervention changes the outcome. The decision can stay with the model inside stated conditions, with explicit act, hold or escalate. Governance evidence follows as a by-product. Three loops, one model. Design: read the trained model and edit it locally. Production: predict, bound, prescribe, explain. In service: controlled, targeted correction from field feedback. No accuracy trade-off. On time series, accuracy is at least equivalent to standard deep learning, frequently superior. Selected validated results across Confiance.AI and client programs: up to 82% fewer input variables, up to 4x faster model development. Independent program evaluation: up to 35x faster inference, models up to 50% smaller. Two modes. Native builds the model in the Xpdeep architecture and closes all three loops. Retrofit explains an existing PyTorch model without changing it: the entry point. Platform. XpViz for data scientists and engineers, XpAct for operations, MCP / API for orchestrators. Cloud, on-premises or embedded. Sectors. Automotive, aerospace and defense, energy, semiconductors, industrial systems, medtech. Deepest edge on multivariate time series and signals. Spun out of the Laboratoire d'Informatique de Grenoble (CNRS, Université Grenoble Alpes).