Understand — Reveal What Your Model Really Learns
Deep learning has long been a black box. Xpdeep opens it. Our self-explainable framework allows teams to visualize, quantify, and interpret what happens inside every layer — turning hidden representations into clear insights that drive model improvement and trust.
Ante-Hoc Explainability That Reveals Real Model Reasoning
Understanding doesn't begin with approximations or reverse-engineered interpretations.
Xpdeep exposes human-friendly, structural explanations directly from the model's internal logic.
Every insight is grounded in ante-hoc transparency — not post-hoc guesses.
No Post-Hoc. Ever.
This enables teams to understand, trust, and improve their models based on explanations that are clear, actionable, and faithful.
Deep learning has long been a black box. Xpdeep makes it transparent with ante-hoc explainability — surfacing the structure of the model's true reasoning in a way humans can understand, challenge, and refine. Instead of inspecting layers or neurons, Xpdeep reveals what the model actually uses to decide, making its behavior intelligible, trustworthy, and actionable.
Fairness & Feature Attribution
Evaluate fairness and understand which factors truly influence decisions using human-friendly, structural attributions. Ante-hoc transparency surfaces real dependencies — not approximated contributions — enabling more equitable, compliant models.
Interactive Heat Maps & Time‑Series
Explore how the model reasons across time and conditions. Interactive visualizations reveal sequential patterns, turning temporal logic into explanations that anyone can understand — without needing to inspect deep learning internals.
Detailed Model Diagnostics
Identify strengths, weaknesses, blind spots, and edge cases through structured diagnostics powered by ante-hoc explainability. Understand how the model generalizes and where it may misbehave — using insights that are directly interpretable by humans.
Understanding a deep model should not require guesswork or technical dissection of layers. Xpdeep provides clear, human-readable explanations that uncover how the model truly reasons — exposing the factors, patterns, and temporal relationships that shape predictions.
With XpViz, Xpdeep surfaces:
- What the model focuses on
- Why it prioritizes some signals over others
- Which reasoning patterns are stable or fragile
- How its behavior changes over time
- Where fairness or drift issues appear
- How alternative scenarios would change outcomes
These insights empower data scientists and domain experts to diagnose issues early, align models with expectations, and improve trust across the organization.
→ See how your model truly reasons — in a way humans can understand, trust, and act upon.
Key Capabilities
Human-readable structural explanations of model reasoning
Automatic detection of biases, drifts, and spurious correlations
Model comparison and version analysis based on explainability quality
Feature-level and concept-level attributions
Explainability for sequences and time-dependent patterns
Understanding your model means controlling it. Xpdeep empowers teams to move beyond accuracy metrics and into transparent, accountable, and explainable behavior. With ante-hoc clarity and human-friendly insights, you can refine, challenge, and validate your models with confidence — long before deployment.
Learn more: Design | Optimize | Explain & Certify | Predict & Explain | Act | XpAct | Time-Series | ROI: Savings | ROI: Growth