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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46%
of organizations are experimenting with AI
14%
have already implemented AI
Build sustainable AI
Succeed in your Artificial Intelligence initiatives
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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Benefits
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
AI assurance: How to avoid an AI crisis with best practices in AI development [2024 update]
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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.
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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