Veritable AI Quality Platform

Our AI Quality Management solutions start with a powerful core.

Strong fundamentals drive great model quality results

Operationalize and Monitor with Confidence Chart

Easily fits your existing environment


model deployment

Business performance

Ensures ongoing model effectiveness

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“We see Veritable as an essential partner… in how we build and operationalize higher quality, trusted AI models faster and more efficiently.”

Vishu Ramachandran, Group Head, Retail Banking
Standard Chartered

Veritable AI Quality Platform Overview

AI Quality management platform mobile
AI Quality management platform

The right platform leads to the best results

Enterprise class explainability

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Based on six years of research performed at Carnegie Mellon University, the Veritable AI Quality Platform performs sophisticated sensitivity analysis that enables data scientists, business users, and risk and compliance teams to understand exactly why a model makes predictions.

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Model quality analytics


Model quality helps ensure models achieve the intended business impact. Veritable analyzes several facets of model quality including:

  • Accuracy
  • Generalization
  • Conceptual soundness
  • Stability
  • Reliability
  • Fairness

Review and governance workflow

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High-stakes and regulated models often require separate model validation or governance processes. Veritable includes best practices for validation and governance, including model documentation, auditability, and reproducibility.

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Model comparisons and selection

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Machine learning development is highly iterative and experimental, so quickly understanding the evolution of model versions is critical. With Veritable, data scientists can more deeply and easily compare models than ever before, enabling them to extract insights to guide faster and more effective model development.

Reporting and monitoring

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Over time, model performance can change due to changes in the underlying data or the implications of that data. The Veritable AI Quality Platform makes it easy for data scientists to monitor and understand data drift, concept drift, and model quality over time.

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Easy to deploy


Veritable embeds easily within your existing infrastructure and workflow

  • Deploy on premises or in your cloud, including private cloud, AWS, Google, or Azure
  • Integrate easily with popular model development and model serving solutions
  • Scale to meet high model volumes
  • Export data via APIs to BI tools such as Tableau and Looker


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