Integrations — Built to Fit Your Infrastructure
The Xpdeep Platform was designed for coexistence. It plugs into existing MLOps environments and enterprise systems, ensuring a frictionless path from experimentation to deployment — without replacing your current stack.
Ante-Hoc Explainability Across Your AI Stack
Xpdeep integrates natively with your existing AI infrastructure while preserving its ante-hoc, self-explainable deep learning architecture.
Unlike solutions that layer post-hoc interpretability after training or rely on monitoring after deployment, Xpdeep brings explainability inside the model from the start.
No Post-Hoc. Ever.
This ensures that every integration — from training to deployment — carries real explainability, trusted optimization, and actionable intelligence.
Seamless Integration With Your AI Workflow
Xpdeep connects easily with modern deep learning environments thanks to native compatibility with:
- PyTorch (core training & model interfaces)
- MLflow (model tracking and reproducibility)
- Airflow (pipeline orchestration)
- Databricks (enterprise data & ML operations)
- Cloud & on-prem environments used in regulated or mission-critical industries
Integrations are designed to keep your existing workflows intact while adding ante-hoc transparency and next-generation explainability to deep models — without rewriting your stack.
Time Series Native Explainability (New)
Deep learning for temporal data is notoriously opaque with post-hoc explainers.
Xpdeep provides native, structural explainability for time-series models — a capability unique to ante-hoc architectures.
This allows teams to understand how, when, and why model reasoning evolves across time.
AI & Data pipelines
Native compatibility with PyTorch, MLflow, Airflow, and Databricks ensures direct integration into your model lifecycle. Whether training or explaining models, Xpdeep connects to your preferred environment instantly.
Enterprise systems
APIs and SDKs let you embed Xpdeep insights into dashboards, control systems, or digital twins. Results and counterfactuals can be automatically pushed to ERP, MES, or CRM systems, closing the loop between AI prediction and operational action.
Deployment flexibility
Deploy anywhere:
- On-premise for security and sovereignty
- Edge for latency-sensitive use cases
- Cloud for scalability and collaboration
Every setup maintains the same explainability, auditability, and traceability guarantees.
Why These Integrations Are Different From Post-Hoc or Observability Layers
Other platforms integrate by adding explainers after model training, or by watching models during deployment to detect issues.
Xpdeep is fundamentally different:
Comparison:
Post-Hoc Explainers:
bolt-on interpretation after training → approximations only
AI Observability Tools:
monitor behavior after deployment → reactive, not structural
Xpdeep Integrations:
connect directly to the model's ante-hoc internal logic, enabling:
- True, structural explainability
- Direct optimization and risk reduction
- Traceability aligned with certification workflows
- Actionability via XpAct
- Lifecycle governance with XpComply (upcoming)
Integrations ensure explainability is not an add-on — it is part of the model's foundation.
A Unified Flow: Understand → Optimize → Act → Govern
Thanks to Xpdeep's ante-hoc architecture, integrations support a unified AI lifecycle:
- XpViz reveals the model's internal reasoning
- XpAct turns insights into operational actions
- XpComply (upcoming) will support governance and documentation
- Time Series Native Explainability deepens this across temporal models
Integrations allow your stack to benefit from cost savings and actionable intelligence at every stage.