THE ENTERPRISE AI COMPANY
We enable organizations to accelerate and scale through all phases in the Development, Operation and Governance of AI.
The AI Governance Handbook
Successfully implementing AI goes hand-in-hand with regulation and guideline compliance. Learn more about the need for AI Governance.
The Grace AI Platform Brochure
To succeed, you need to scale and accelerate multiple AI projects across your organization; for this, you need support from an Enterprise AI platform.
Robust and proven AI models
We help accelerate and scale your AI projects. These models could help get you started:
Analyzing past client lifecycles and the likelihood of churn
Determining the right price for a product or service
Lowering acquisition costs and extending customer lifetime
Automatically routing messages to the correct recipients
Screening documents for critical or forbidden phrases
Identifying tickets with the highest importance
Analyzing customer data to identify high-value clients
Classifying fraudulent transactions
PREDICTION OF LOAN REPAYMENT
Predicting payment ability to prevent non-paying customers
INSURANCE CLAIM REJECTION
Determining which claims to process and which to reject
We support your AI adoption
See how we have helped others provide stability, scalability, and flexibility in their AI projects.
The Enterprise approach to AI
Leverage standardized processes and workflows across your AI projects, including full model lifecycle management, combined with an AI Governance solution, that supports all aspects of the responsible use of AI.
Remaining compliant with regulatory requirements is critical in the AI life-cycle. A key element here is AI Governance.
Ensure an efficient, secure, and robust AI implementation, by standardizing processes and workflows across AI projects.
It’s not fake, it’s not artificial, it’s real news
Twitter recently came under fire due to structural bias in their cropping algorithm used to crop photos for the Twitter feed.
Every day, more and more decisions are made across the enterprise, and many of these decisions are made by algorithms within AI systems. Humans can unconsciously bring biases into their decision-making, and we must ensure that these are not reflected or further...
Bias in AI is and remains an increasing phenomenon. To overcome bias, you need to apply the aspect of fairness. This article will examine the legal frame, looking into specific nuances of fairness in AI.
The Ethical AI Newsletter
Sign up for our Ethical AI newsletter and receive the latest AI insights from our data science and AI experts.