Enabling people and machines to make better decisions together.
Helping machine learning achieve its promise
Veritable fills a critical gap in your AI stack, explaining and testing model quality throughout the lifecycle.
Veritable’s AI Quality solutions explain, debug, and monitor machine learning models, leading to higher quality and trustworthiness, as well as faster deployment. Backed by years of pioneering research, Veritable works across the model lifecycle, is independent of model development platforms, and embeds easily into your existing AI stack.
With major offices in the US, the UK, and Singapore, Veritable is ensuring that machine learning delivers value and benefit to organizations, customers, and citizens alike.
Veritable has garnered accolades recently for explainability and model quality management
What drives us
We thrive on solving hard, meaningful problems with advanced innovation.
Innovative and Talented People
When you work with creative, dedicated individuals, the impossible becomes possible.
Trust and Confidence
We believe that trustworthy, transparent AI can positively impact the world.
Fairness and Transparency
We are passionate about the responsible use of AI and reducing algorithmic discrimination.
Veritable Executive Team
The Veritable Story: United by Common Purpose to Improve AI Outcomes
The Veritable story began in 2014 at Carnegie Mellon University with Anupam Datta, a Professor of Computer Science and Electrical & Computer Engineering, and Shayak Sen, a PhD student. In the course of their research, Anupam and his team discovered gender bias in online advertising. However, they lacked the tools to explain precisely what caused the bias. Passionate about enabling effective and trustworthy artificial intelligence, Anupam, Shayak, and their colleagues dug deeply into the issue, creating a body of work on explainable artificial intelligence. Shayak’s hands-on experience with building and deploying machine learning models in the financial services industry informed the team’s approach. Operating on the thesis that explainability is key to guard against societal harms and to build higher quality models, Anupam, Shayak, and their team became part of a growing interdisciplinary community tackling these challenges and raising awareness about them in academic, regulatory, and public spheres.
In parallel, Will Uppington was experiencing a similar set of challenges. A member of the founding team at Bloomreach, Will was productionizing black-box machine learning models and algorithms in enterprises. Will was struck with the fact that data scientists did not have sufficient visibility to evaluate and improve model quality during model development and in production. When business users demanded explanations for unexpected model predictions, existing tools did not provide adequate answers.
Anupam and Shayak were introduced to Will by a mutual friend in late 2018. It was a meeting of minds. All three came together to form Veritable in early 2019 to analyze, improve, and build trust in machine learning models. By the autumn of 2020, the company emerged from stealth and today serves enterprises in multiple industries across the globe.
2000 Broadway #330
Redwood City, CA
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Veritable UK Limited
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#14-04 Singapore Business Federation Centre
Veritable (India) Private Limited
WeWork, Block L,
Outer Ring Rd, Bengaluru, Karnataka 560103