Dataset services
We collect, clean, label and organise large datasets for AI model training, with augmentation, class balancing, documentation, quality assessment and compliance for robust, secure data.
- Data collection and sourcing
- Identifying and sourcing reliable datasets from open or licensed repositories, compliant with data usage regulations such as licensing and GDPR.
- Data cleaning and preparation
- Handling missing and inconsistent data, removing outliers and irrelevant entries, and standardising and normalising data for ML readiness.
- Data augmentation
- Image augmentation (rotation, cropping, flipping), text augmentation (synonym replacement, paraphrasing) and synthetic data to balance and strengthen datasets.
- Class balancing
- Oversampling minority classes with SMOTE or similar methods, undersampling majority classes, and combining both for optimal balance.
- Annotation and labelling
- Manual and automated labelling tools, crowdsourced annotation, and label accuracy checked through inter-annotator agreement.
- Documentation and metadata
- Comprehensive dataset documentation with metadata and usage guidelines, plus data lineage and versioning for reproducibility.
- Quality assessment and improvement
- Assessing completeness, accuracy and representativeness, detecting data leakage, and benchmarking datasets against baseline models.
- Security and compliance
- Anonymisation and privacy protection, adherence to GDPR and HIPAA, and safeguarding intellectual property in proprietary datasets.