Data Labeling & Foundation by DataXWorks

DataXWorks supports organizations in building and scaling AI systems by delivering structured datasets, high-quality data annotation, and human-in-the-loop validation. Designed to work within AWS environments, DXW enables teams to prepare, label, and continuously improve data used for machine learning, computer vision, NLP, and large language models.

DXW works with customers to create AI-ready datasets aligned to specific use cases. This includes preparing structured and unstructured data such as text, images, video, audio, and LiDAR, along with defining taxonomies, classification structures, and data standards. DXW also supports prompt and response structuring for LLM training, along with data cleansing, normalization, and enrichment to ensure consistency.

Data Annotation Services

DXW provides data annotation services that convert raw data into accurately labeled datasets required for model training and evaluation.

Image and video annotation includes semantic segmentation, 2D and 3D bounding boxes, polygons, object tracking, key point annotation, and classification.

LiDAR and 3D annotation includes 3D cuboids, semantic segmentation, and point cloud, landmark, and polyline annotation.

Text and NLP annotation includes Named Entity Recognition, sentiment and intent analysis, classification, enrichment, and deduplication.

Document AI services include document classification, data extraction, OCR validation, and structured labeling.

Content services include moderation, filtering, and quality assessment.

DXW’s annotation workflows are designed to ensure consistency, accuracy, and scalability across different data types and industry use cases.

Validation and Quality Control

DXW integrates human-in-the-loop validation into all workflows to maintain data quality and improve model performance. This includes multi-level review processes, error identification, confidence scoring, and structured feedback loops. DXW also supports prompt evaluation and response validation for large language models.

Delivery Model

DXW operates as an extension of customer teams and integrates with AWS tools and workflows. Engagements typically begin with a pilot dataset to validate quality and refine annotation guidelines. Delivery is supported by dedicated program management, scalable teams, and secure, compliant operations. DXW works alongside AWS services such as data pipelines, storage, and labeling tools to enable execution within existing environments.