Secure and enhance your AI software
Optimize and safeguard your AI systems or code with Sigrid and our expert AI assurance services, ensuring they are robust, compliant, and performance ready.
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46%
of organizations are experimenting with AI
14%
have already implemented AI
Build sustainable AI
Neglecting AI software best practices can lead to costly mistakes
Build and deploy responsible AI with SIG
Build resilient AI systems
From enhancing AI readiness to AI model validation, we offer assurance services throughout your entire AI system journey. Our tools and expertise ensure your AI projects meet the highest standards of quality, security, and effectiveness.
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Optimize AI code generation
We provide essential insights and practical tools to ensure your AI-generated code is high-quality, scalable, and compliant. Optimize your development workflows and accelerate innovation securely.
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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.
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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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