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Software Assurance for AI

In Artificial Intelligence (AI), solid engineering and code quality are difficult to achieve but essential for success. We have extended our core capabilities to help you with your AI initiatives to design, build and deploy responsible AI and to be truly successful.

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of organizations are experimenting with AI


have already implemented AI

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Build sustainable AI


Succeed in your Artificial Intelligence initiatives

Too often we witness inspiring AI initiatives fail due to trust issues, high time pressure, unfamiliar paradigms and typical lack of software engineering best practices.

We can enable your AI to succeed:

Transition from data science to AI in practice

Transform an engineering effort into practical application where quality is essential by providing insight into the maturity of both software and processes and offer improvement guidance.

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Gain trust in your AI

Increase confidence in AI by conducting in-depth assessments of AI software and processes to assure the solution is accurate, robust and supports legal, ethical and regulatory compliance.

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Understand the economics of AI engineering

We help manage changeability and testability, or valuate AI solutions through understanding of required effort, cost and involved risk.

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Insight into complete health of product and processes

We carry out a thorough analysis on both your AI product and software engineering process. To reveal hidden risks within your AI, we assess the complete software health from different perspectives like Maintainability, Security, Privacy, Performance Efficiency and Reliability.

Actionable and independent AI-specific advisory

Our AI experts provide guidance ranging from code level security improvements to high level strategic technology advice. Independent, impartial and objective. By understanding the main technical impediments we help to develop a long-term technology roadmap and guide you towards execution while aligning with your business goals.

Continuous transparency of development

Our advisory services are supported by our leading software assurance platform – Sigrid®. With Sigrid®, developers, architects and other IT stakeholders get centralized access to our findings on your applications to help you stay on top of performance.

Insight in the global market

Based on the analysis of more than 200 billion lines of code in more than 300 technologies, we help you understand your competitive position compared to the global market. We have collected a vast knowledge of best and bad practices in software engineering, specifically in machine learning, optimization and other data-intensive applications.

Our Clients.

Who we help

Further reading

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ISO/IEC 5338: Get to know the global standard on AI systems

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Averting a Major AI Crisis: Fix the Big Quality Gap in AI Systems

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How Artificial Intelligence attacked my family and other AI security lessons

The attack of the voice assistant at my home demonstrated two aspects of AI: it is “potentially autonomous”, and it displays “emergent behavior”. The question is how organizations can build secure AI systems based on their characteristics. My response is that it helps to treat AI just like any IT while understanding a few caveats. 

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AI engineering practices in the wild

AI projects are, at their core, software engineering projects. In our research on the topic, we've identified best practices for designing and deploying responsible, successful AI – and sat down with the development team at Kepler Vision Technologies to learn how these practices are applied.

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Taking Artificial Intelligence out of the quicksand

One of the big challenges nowadays in AI is the ability to change. Implementations of AI are notoriously hard to keep up to date because software engineering best practices are typically ignored during the enormous effort of preparing the data. This causes AI initiatives to fail, unless software quality is seen as the enabler.

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