Question 1

What is Xpdeep in simple terms?

Accepted Answer

Xpdeep is a deep learning framework that creates self-explainable models — architectures where explainability is embedded inside the model itself, not reconstructed afterward.Every prediction comes with a human-readable explanation, confidence indicators, and complete traceability.You get AI that performs, explains itself, and can be trusted in high-stakes environments.

Question 2

How is Xpdeep different from SHAP, LIME, DeepLIFT, or Captum?

Accepted Answer

Post-hoc tools (SHAP, LIME, Captum…) generate approximations after training.Their explanations are often incomplete, unstable, or inconsistent with the model's true reasoning.Xpdeep is different:It builds ante-hoc self-explainable models — transparency is part of the architecture.Explanations are exact, stable, and consistent with the model's internal logic.Xpdeep provides prescriptive "How-To" insights (counterfactuals) natively.No need for external explainers or patches.No Post-Hoc. Ever.

Question 3

What does "self-explainable model" mean?

Accepted Answer

A self-explainable model is one that reveals the logic behind each decision as part of the model itself.It doesn't need external probes or attribution tricks.Instead, it produces:Human-readable explanationsClear drivers and contributing factorsActionable "here is how to improve this outcome" reasoningIt's the difference between a doctor giving you a diagnosis — and a doctor showing you exactly why.

Question 4

Does using Xpdeep reduce model performance?

Accepted Answer

No.Ante-hoc explainability often improves performance because the model learns structured, constraint-aligned reasoning.Teams reach the optimal model faster, and error sources become visible and fixable.

Question 5

What types of data and tasks can Xpdeep handle?

Accepted Answer

Xpdeep supports:Tabular dataTime-series & sensor dataTextImagesMultimodal modelsIt works for classification, regression, anomaly detection, forecasting, ranking, and more.Its native strength is time-series and temporal modeling, where Xpdeep offers the only ante-hoc explainability framework that works without post-hoc approximations.

Question 6

Does Xpdeep support LLMs?

Accepted Answer

Yes.Xpdeep can explain fine-tuned enterprise LLMs by reconstructing the reasoning of the adapted model.For smaller transformers, Xpdeep can produce direct self-explainable models.For larger LLMs, Xpdeep generates structural, certifiable explanations of the adapted model's behavior.

Question 7

Can Xpdeep explain already-trained models?

Accepted Answer

Yes.You can upload an existing PyTorch model and obtain:Ante-hoc style structural explanationsMore faithful insights than SHAP/LIMEActionable How-To analysesFull governance & audit logsThis is the basis of Explain Existing Models.

Question 8

Does Xpdeep require changing development environments?

Accepted Answer

No.Xpdeep is a PyTorch-native library.Your pipelines, notebooks, and data loaders stay the same.You can:Build new models with the Xpdeep APIImport existing PyTorch modelsRun inference + explainability via Python or REST APIsUse the Hub (SaaS) for visualization & governance

Question 9

What does it mean that your AI is "actionable"?

Accepted Answer

Conventional AI stops at predictions.Xpdeep goes further with prescriptive intelligence:Shows exactly why a decision was madeShows how to change the outcome (counterfactuals)Allows real-time "what-if" explorationGuides operators with clear next stepsSupports optimization loops and parameter adjustmentActionable AI = Not just predictions → Decisions you can justify and improve.

Question 10

How does Xpdeep help with AI Act, banking, and health compliance?

Accepted Answer

Xpdeep automates the evidence regulators expect:Transparent explanations ("why" behind decisions)Bias, fairness, and drift indicatorsFull traceability of parameters, data versions, and model logicAudit logs for each inferenceDocumentation for model approvals (AI Act, SR 11-7, ISO, GxP, GDPR)Compliance is not a separate step — it is built into the model lifecycle through ante-hoc explainability.

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Frequently Asked Questions

Technology

What is Xpdeep in simple terms?

How is Xpdeep different from SHAP, LIME, DeepLIFT, or Captum?

What does "self-explainable model" mean?

Does using Xpdeep reduce model performance?

What types of data and tasks can Xpdeep handle?

Does Xpdeep support LLMs?

Can Xpdeep explain already-trained models?

Does Xpdeep require changing development environments?

Compliance

How does Xpdeep help with AI Act, banking, and health compliance?

Deployment

Can Xpdeep be deployed on-premise or at the edge?

Can Xpdeep work with our existing infrastructure?

Getting Started

How do we get started?

What industries does Xpdeep serve?

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