The data foundation for AI from training through deployment.

Sinorax AI supports multimodal AI data workflows across images, video, audio, speech, and text. Depending on the project, this can include bounding boxes, polygons, classification, semantic segmentation, keypoints, transcription, text annotation, and other task-specific labeling requirements.

Yes. We can help collect, organize, clean, annotate, and prepare datasets around your specific AI requirements. This may include defining data coverage, identifying edge cases, establishing annotation guidelines, and preparing datasets for model training, testing, or evaluation.

High-quality data helps models learn the correct patterns and reduces the risk of inconsistent labels, missing information, duplicates, and irrelevant samples. Data quality, diversity, coverage, and consistency should be considered together rather than focusing only on dataset size.

There is no universal dataset size that works for every AI project. The required volume depends on the model, use case, complexity of the task, number of classes, environmental conditions, and expected performance. We can help determine an appropriate data strategy based on your project requirements.

Real-world data often contains difficult examples such as occlusion, poor lighting, unusual objects, ambiguous labels, background noise, and rare scenarios. Sinorax AI can incorporate edge cases and project-specific annotation rules into the dataset workflow to improve coverage and consistency.

Yes. An existing dataset can be reviewed, cleaned, restructured, re-annotated, and expanded based on the project's requirements. This can include identifying inconsistent labels, duplicate samples, missing annotations, low-quality data, and coverage gaps.

Yes. We can prepare structured datasets for testing, validation, benchmarking, and performance evaluation. Evaluation data can be designed to represent different environments, scenarios, classes, and edge cases so that model performance can be assessed beyond the training dataset.

A ready-made dataset is useful when your project can work with an existing dataset and requires a faster starting point. A custom dataset is more suitable when your AI system requires specific objects, environments, regions, industries, annotation standards, or real-world scenarios that general datasets do not adequately cover.

We consider the environments, scenarios, demographics, geographic conditions, and other relevant factors that may affect the target AI system. Dataset composition can then be reviewed to identify under-represented scenarios, excessive repetition, and potential coverage gaps.

Data ownership and usage rights are defined according to the applicable project agreement. Sinorax AI treats client data as confidential and does not use client-provided data to train third-party models or share it with external parties without appropriate authorization.

Yes. Sinorax AI can support multiple stages of the AI lifecycle, including data collection, data preparation, annotation, dataset development, model training support, testing, validation, and performance evaluation.

Share your AI use case, available data, target model, or project requirements with us. We can help identify the required data coverage, annotation approach, quality requirements, and workflow before the project is scaled.