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:

CHURN PREDICTION

Analyzing past client lifecycles and the likelihood of churn

PRICE PREDICTION

Determining the right price for a product or service

LEAD QUALIFICATION

Lowering acquisition costs and extending customer lifetime

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MESSAGE ROUTING

Automatically routing messages to the correct recipients

PHRASE DETECTION

Screening documents for critical or forbidden phrases

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TICKET SORTING

Identifying tickets with the highest importance

CUSTOMER CLASSIFICATION

Analyzing customer data to identify high-value clients

FRAUD PREDICTION

Classifying fraudulent transactions

PREDICTION OF LOAN REPAYMENT

Predicting payment ability to prevent non-paying customers

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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.

Governance

Remaining compliant with regulatory requirements is critical in the AI life-cycle. A key element here is AI Governance.

Platform

Ensure an efficient, secure, and robust AI implementation, by standardizing processes and workflows across AI projects.

Models

Are you interested in standard models or perhaps looking into expanding to more advanced AI model development? 

It’s not fake, it’s not artificial, it’s real news

Disparate impacts in AI implementations

Disparate impacts in AI implementations

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...

Fairness in AI

Fairness in AI

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.

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