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.
Comprehensive Online Documentation
Xpdeep provides full, continuously updated online documentation covering the framework architecture, APIs, design patterns, and end-to-end examples. Engineers can explore how ante-hoc explainability is implemented at the model level, how audit metadata is generated, and how governance constraints are enforced by design.
Access the documentation directly at https://docs.xpdeep.com/latest/.
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.
Designing with Explainability as a First-Class Constraint
Xpdeep embeds a self-explainable layer directly into the model architecture, aligning business objectives, operational constraints, and technical metrics from the very first iteration. Explainability is not added after training — it is a structural property of the model itself.
For AI engineers and data scientists, the experience remains entirely familiar. Xpdeep mirrors standard PyTorch syntax and workflows: models are designed, trained, and optimized as usual, but every training run automatically produces internal transparency, versioned structure, and a complete audit trail.
This approach shortens design cycles, reduces debugging and rework, and enables smoother collaboration between technical teams and business or risk stakeholders. Models are assessment-ready by construction — whether for internal review, external validation, or formal certification.
Developers can explore the full API, reference architectures, and implementation examples in the online documentation: https://docs.xpdeep.com/latest/
→ Design smarter. Build ante-hoc models that justify themselves — without post-hoc explainers.
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.