TAPP Security provides an AI explainability platform that integrates with major ML frameworks to generate model‑agnostic interpretation artifacts such as SHAP, LIME, and counterfactual analyses. The platform delivers automated compliance reports, interactive visualizations, and real‑time drift detection, and supports on‑premise, private‑cloud, or SaaS deployments to satisfy regulatory requirements.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises deploying machine‑learning models often lack transparent insight into how predictions are generated, creating compliance gaps, debugging challenges, and reduced stakeholder trust. Without systematic explainability, regulatory audits and risk assessments become costly and error‑prone.
Solution
TAPP Security delivers an AI explainability platform that instrumentally analyzes black‑box models and produces interpretable artifacts for both technical and non‑technical audiences. The service integrates with common ML frameworks (TensorFlow, PyTorch, Scikit‑learn) and extracts feature‑importance metrics using model‑agnostic algorithms such as SHAP and LIME. Generated explanations are packaged into automated compliance reports and visual dashboards that can be embedded in existing MLOps pipelines. Real‑time monitoring flags drift or anomalous decision patterns, enabling rapid remediation. APIs and SDKs allow teams to embed explainability checks directly into CI/CD workflows, ensuring that transparency is enforced throughout the model lifecycle. The platform supports on‑premise, private‑cloud, or SaaS deployments to meet diverse data‑governance requirements.
Target Audience
Primary users are data science and MLOps teams, as well as risk and compliance officers in regulated sectors such as finance, healthcare, and insurance, who require auditable model transparency.
Features
- Model‑agnostic explainability engine supporting SHAP, LIME, Integrated Gradients, and counterfactual analysis
- Automated compliance report generator with audit‑trail logging and export to PDF/JSON formats
- Interactive visualization suite (feature attribution heatmaps, decision trees, partial dependence plots) accessible via web UI or embedded iframe
- SDKs for TensorFlow, PyTorch, Scikit‑learn, and ONNX enabling plug‑and‑play integration into training and inference pipelines
- Real‑time drift detection and alerting that surfaces shifts in input distribution or explanation stability
- Role‑based access control and end‑to‑end encryption to satisfy GDPR, HIPAA, and industry‑specific regulations
- Flexible deployment options: on‑premise Docker/Kubernetes, private‑cloud VM, or managed SaaS tenancy