Design — Build Explainable Models From the Start
Xpdeep automatically generates self-explainable deep learning models. Each model includes an audit trail and detailed metadata, accelerating model design, reducing debugging time, and ensuring transparency. With explainability built in, your models are ready for governance from day one.
Ante-Hoc Explainability From the First Training Epoch
Designing with Xpdeep means explainability is embedded before any prediction or optimization.
Every model begins with ante-hoc, structural transparency — exposing internal logic from the very first training epoch.
This eliminates the inconsistencies of post-hoc explainers and creates models that are transparent, governable, and audit-ready by construction.
No Post-Hoc. Ever.
With Xpdeep, explainability is not an afterthought — it is the foundation of model design. Our framework enables data scientists to build deep models that are transparent by construction, natively compatible with PyTorch, and aligned with governance requirements from day one. Explainability begins at the architectural level through an ante-hoc explainability layer, ensuring that every model you design is internally interpretable, auditable, and ready for certification.
Automatic Explainability Layer
Every neural network generated by Xpdeep includes a built-in, ante-hoc explainability layer. Transparency is embedded during training — no post-hoc tools required. This structural integration allows every model to justify its decisions from the start.
Built-in Audit and Traceability
Xpdeep automatically logs model versions, structural metadata, and decision pathways, enabling reproducible development and simplifying compliance, safety checks, and debugging.
Governance From Design
Start with models that meet governance and regulatory expectations before deployment. Xpdeep's governance-by-design ensures that every model is explainable, traceable, and review-ready from the first iteration.
At Xpdeep, explainability starts at the design phase — not as an afterthought.
Our framework embeds a self-explainable layer into every deep model, linking business goals, operational constraints, and technical metrics from the first iteration.
For AI engineers and data scientists, Xpdeep feels completely natural: its syntax and workflow mirror standard PyTorch. You design, train, and optimize models as usual, but every model is accompanied by true internal transparency and a full audit trail.
This leads to faster design cycles, smoother collaboration between technical and business teams, and immediate readiness for assessment, validation, or certification.
Developers can explore the full API and examples in the online documentation.
→ Design smarter. Build ante-hoc models that justify themselves — without Post-Hoc.
Key Capabilities
Native PyTorch integration — same syntax, same workflows
Frugal learning algorithms — fewer inputs with equal or better accuracy
Automatic explainability layer creation — ante-hoc transparency built-in
Built-in audit and traceability metadata — every model version is tracked
Designing with Xpdeep means trust is never retrofitted. Every model is ante-hoc explainable, governable, and ready for real-world deployment. You never have to patch transparency onto a black-box again.