More efficiency. Less labels.
Helping organisations solve real world problems
The Opportunity
Data labelling is one of the costliest processes in machine learning. Finding, managing and labelling data to build a sufficiently performing model can take weeks or months and in some cases requires sophisticated human expertise.
The Vision
We empower AI and ML professionals to build their ML models with less labelling effort through smart data selection, synthetic data generation and data augmentation.
The Benefit
Our prototype API minimizes human interaction by smartly selecting the bare minimum amount of data for the domain expert to label.
Proprietary querying strategy
A proprietary querying strategy that reduces the amount of labeled data needed
Active learning approach
Active learning approach that maximizes accuracy and efficiency
Use of representation models
Use of representation models to reduce processing time and computing power required
Customizable and flexible solution
Customizable and flexible solution that can fit seamlessly into your existing MLOps pipeline
Team
We are a team of five professionals with experience in machine learning, semantic technologies and data engineering. Our vision is to build a world that enables humans and AI systems to understand each other and to co-create a trustworthy environment that enhances each other’s complementary capabilities.