XpAct — Turn Explainability Into Action
XpAct connects AI insights to real-world KPIs. It combines explainable predictions and prescriptions with an integrated LLM that interprets and comments on results — turning complex model behavior into clear, actionable guidance accessible to every stakeholder.
Ante-Hoc Explainability That Powers Real-World Action
XpAct is built on Xpdeep's ante-hoc, self-explainable deep learning framework.
Unlike traditional systems that rely on post-hoc approximations, XpAct receives insights based on the model's true internal reasoning.
No Post-Hoc. Ever.
This foundation enables reliable, traceable, and certifiable actions — grounded in the real logic of the model, not in reconstructed interpretations.
Understandable by All, Not Just AI Specialists
Explanations in XpAct are designed for comprehension across disciplines.
Whether you're a data scientist, business owner, engineer, or risk officer, XpAct translates model reasoning into intuitive, human-readable insights.
Each analysis provides context-aware commentary generated by a dedicated LLM prompt — explaining not only what the model predicts, but also why and how it could improve.
Explainability That Becomes Actionable Intelligence
XpAct transforms deep learning outcomes into operational, aligned, and auditable actions.
Because Xpdeep models are ante-hoc by design, XpAct can:
- Trigger actions based on the model's true causal factors
- Prioritize interventions grounded in the model's internal dynamics
- Recommend optimized next-best-actions with full transparency
- Guarantee traceability for regulatory and business requirements
- Provide decisions that are explainable, certifiable, and enterprise-ready
This enables measurable ROI through new growth opportunities and operational cost savings, while maintaining full visibility through XpViz and compliance via XpComply.
From Explainable AI to Explainable Business
XpAct bridges the gap between data science and business impact:
- •Link AI outputs to measurable KPIs like quality, yield, risk, uptime, or pricing fairness.
- •Surface model biases, performance gaps, and risk factors through explainable visualizations.
- •Enable prescriptive actions — adjustments, corrective measures, or "how-to" optimizations derived directly from Xpdeep's counterfactual engine.
These capabilities make model results tangible, auditable, and aligned with operational objectives.
A Unified Experience With XpViz
Every model available on the platform can be accessed through both XpViz and XpAct:
- XpViz —for in-depth technical analysis and explainability across training epochs.
- XpAct —for decision-ready insights, contextual explanations, and guided recommendations.
Together, they form a continuous workflow from model exploration to real-world action.
Different From AI Observability and Post-Hoc Explainability
Other systems either:
- Explain a black-box model after the fact (post-hoc XAI), or
- Monitor models after deployment (AI observability).
XpAct is fundamentally different:
Comparison:
- Post-Hoc XAI: Approximates explanations; unreliable for automated decisions
- AI Observability: Detects issues after the system is already live
- XpAct: Receives true, ante-hoc explanations → enabling safe, traceable action in real time
This is the cornerstone of trusted actionable intelligence.
Native Support for Time-Series Actions
For temporal models, XpAct uses Xpdeep's native time-series explainability to detect when and why specific patterns emerge — enabling proactive interventions for operations, maintenance, safety, and risk reduction.
Example Use Case: Guided & Compliant Pricing
In this example, XpAct analyzes a deep learning model used for insurance pricing.
It explains feature impact differences between customer groups and automatically generates a clear, business-oriented summary of potential bias and corrective levers.
The interface enables risk officers, compliance teams, and business managers to validate fairness, understand causes of divergence, and take corrective action — without needing to read a single line of code.
XpAct allows enterprises to operationalize deep learning with full transparency and No Post-Hoc approximations. Enable actions that are traceable, certifiable, and grounded in the model's true reasoning — not estimations.
XpAct makes explainability operational — helping organizations move from understanding AI to confidently acting on it.
See XpAct in Action
Experience how explainable predictions and prescriptions connect to real-world KPIs.
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XpAct is included in all paid subscription tiers, with growing quotas and capabilities as your adoption expands.