Explain Existing Models — Without Rebuilding Them
Many enterprises already have deep learning models in production — yet cannot fully explain, certify, or trust them. With Xpdeep, you can reconstruct the explainability of your existing PyTorch models to make them transparent, auditable, and ready for deployment in critical or regulated environments.
No Post-Hoc — Explainability From the Inside
Xpdeep explains existing deep models using its ante-hoc, self-explainable deep learning engine.
Unlike traditional post-hoc methods that approximate a black-box model's behavior, Xpdeep reconstructs the true internal decision pathways of your model.
No Post-Hoc. Ever.
This enables explanations that are precise, trustworthy, and aligned with what the model actually does — not what an external explainer guesses.
Explain Your Existing PyTorch Models with Real Transparency
Upload your trained model through the Xpdeep code interface and uncover the true internal mechanisms behind its predictions.
Whether your model is tabular, temporal, multimodal, or sensor-based, Xpdeep analyzes its core decision structure through an ante-hoc framework designed to surface the actual dependencies, sensitivities, and internal logic.
This helps you:
- Understand why the model predicts what it predicts
- Detect hidden dependencies, biases, or spurious correlations
- Reduce features or sensors without losing performance
- Simplify certification and documentation
- Communicate model behavior clearly to internal and external stakeholders
Xpdeep transforms black-box models into transparent, certifiable systems — even if they were not originally designed to be explainable.
Available Today Through Code Execution
You can already use Xpdeep to explain your existing deep learning models today.
The current workflow is code-based and runs directly in your development environment or notebook using the Xpdeep API.
A fully automated, UI-driven experience will be introduced progressively.
For now, the code workflow provides developers with direct access to the complete ante-hoc explainability engine — with No Post-Hoc approximations and full visibility into the model's internal reasoning.
Why This Is Different from SHAP, LIME, Captum, and Post-Hoc XAI
Post-hoc explainers analyze outputs after inference.
They cannot access the model's internal computations, so they approximate, interpolate, and simplify — often producing unstable or contradictory explanations.
Xpdeep is fundamentally different:
Comparison:
Post-Hoc Libraries (SHAP, LIME, Captum):
- Approximate the model's logic
- Unreliable for certification or high-risk decision-making
- Cannot support deep architecture optimization
Xpdeep (Ante-Hoc Framework):
- Reveals the model's actual decision structure
- Produces stable, structural explanations
- Enables model optimization, simplification, and governance
- Aligns with the transparency requirements of regulated industries
Xpdeep does not explain the output — it explains the model itself.
Explainability That Leads Directly to Action
Xpdeep links explanations to actionable intelligence by revealing:
- Which variables truly drive predictions
- Which features can be removed without performance loss
- Which internal pathways are fragile or error-prone
- How to optimize the model for accuracy, robustness, or fairness
- How to reduce operational costs while improving reliability
This creates a full pipeline from explanation → analysis in XpViz → action in XpAct → governance in XpComply.
This enables operational cost savings and new growth opportunities across industries like automotive and predictive maintenance.
Native Support for Time-Series Models
Temporal deep learning models are extremely difficult to explain with post-hoc methods.
Xpdeep is the first framework to offer native structural explainability for time-series, allowing you to understand when and why predictions evolve over time.
When Should You Use Xpdeep to Explain an Existing Model?
Xpdeep is ideal when you need to:
- Validate a model before deployment
- Make a model certifiable for regulated environments
- Diagnose unexpected or unstable behaviors
- Reduce variables or sensors
- Improve trust with stakeholders
- Prepare documentation for QA, safety, or compliance
- Support AI Act transparency requirements
- Optimize a model without retraining from scratch
This brings clarity, structure, and trust to existing deep learning systems.
Why Explain Existing Models?
Unlock Hidden ROI
Existing models represent significant investment — but without explainability, their value remains locked. Xpdeep reveals what drives each model's decisions, identifies weaknesses such as false positives and false negatives, and highlights opportunities for targeted improvement.
De-Risk Critical Deployments
Unexplained AI is risky AI. Whether for compliance, safety, or quality assurance, Xpdeep enables transparent auditing and validation so that decision-makers can evaluate and certify model behavior with confidence.
How It Works
Upload your PyTorch model and let Xpdeep analyze it using its self-explainable framework.
The system reconstructs a faithful explainability layer around the model, providing:
- Model-level and prediction-level explanations
- "How-To improve" analyses for optimization
- Automated compliance and documentation reports
No retraining, no code modification — just complete, interactive insight through XpViz and XpAct.
Explainability is not interpretation — it is a structural understanding of the model itself.
Xpdeep reveals this structure with No Post-Hoc approximations, enabling deep, trustworthy analysis and enterprise-grade decision-making for your existing models.
Results
Once explained, your existing model becomes:
- Transparent — its inner logic and key variables are understood
- Auditable — every decision trace is logged and justifiable
- Certifiable — documentation and governance artifacts are automatically generated
You recover trust, compliance, and ROI from the models you already own.