We bring together AI and industry expertise to help teams build better computer vision models with synthetic data.
Better Data. Better Vision AI.
Building reliable computer vision starts with having the right training data. Collecting and annotating real-world images can be time-consuming, expensive and particularly difficult when defects or unusual conditions rarely occur.
At syntheticAIdata, we help organisations address these challenges by creating synthetic images tailored to their applications. Combining 3D technology, AI and software engineering, we empower teams with synthetic data tailored to their specific needs.
Our focus is practical: helping customers move from an initial idea to training data that supports real-world development, testing and improvement.
Our Mission
Empower Teams to Build Better Vision AI with Synthetic Data.
Our mission is to make synthetic data practical and accessible, whether you are a developer building your first computer vision model or an enterprise solving complex industrial challenges.
For developers, we provide an easy-to-use platform to generate annotated training images, explore different scenarios and bring synthetic data into existing workflows. This reduces the effort spent collecting and labelling images, leaving more time to build and improve models.
For enterprises, we provide a synthetic data generation solution that integrates directly into their existing systems and workflows. Tailored to specific use cases and technical requirements, it enables teams to generate and manage training data within their own processes, from industrial defect detection to specialised inspection applications.
Our Values
Customer Focus. Open Collaboration. Earned Trust.
We believe meaningful innovation starts with understanding the problem. That is why we work closely with customers and partners to understand their applications, constraints and goals before shaping a solution.
Curiosity drives us to explore new approaches, while practical thinking keeps us focused on what is useful. We value clear communication, honest feedback and the willingness to learn through experimentation.
Trust, empathy and diverse perspectives are equally important to how we work. We aim to build lasting relationships where people can share ideas openly, challenge assumptions and contribute to better outcomes.
Our Partnerships and Recognition
Working Together to Advance Vision AI.
As a Strategic Partner of the Edge AI Foundation, we contribute to a global community advancing edge AI through shared knowledge and collaboration. Our work has also been recognised with the Microsoft Partner Award 2024 in Denmark, in the Microsoft for Startups category.
Through collaborations with companies such as Danish Technological Institute, HP and Arduino, we bring synthetic data into practical applications across computer vision, industrial inspection and robotics. These projects connect our technology with real-world challenges and help demonstrate how synthetic data can support the journey from development to deployment.
Responsible AI
Responsible Data. Reliable Evaluation. Transparent Practices.
Responsible AI development requires careful attention to the data used to train and evaluate models. Synthetic data gives teams greater control over the scenarios represented in their datasets, helping them investigate gaps and include conditions that might otherwise be overlooked.
We encourage a thoughtful approach to dataset design, with clear objectives and an understanding of what generated images can and cannot represent. Greater variety can support broader coverage, but synthetic data alone does not guarantee accuracy, fairness or real-world reliability.
Validation against representative real-world data remains essential. Our aim is to help customers use synthetic data as part of a transparent, iterative development process, with informed decisions about performance and limitations.
Built Around Your Challenges
Every computer vision project has its own requirements. The objects being recognised, the defects being inspected and the cameras being used all influence what effective training data should look like.
From industrial inspection to robotics, we work with teams to translate these requirements into relevant synthetic datasets. Whether you need to supplement existing images, explore a new use case or generate examples of hard-to-capture conditions, we can help you identify a practical starting point.
Reach out anytime. Let's discuss your project and how synthetic data can help.