Leverage Labelbox’s platform to build world-class NLP models that help you unlock revenue, improve intelligent applications and drive customer satisfaction.
Combine the power of a data engine with text labeling tools to quickly tag text strings, conversations, paragraphs and more.
Walmart wanted to enhance the efficiency of generating labeled data for its conversational AI and large language model (LLM) applications. Their data science team aimed to streamline the annotation of conversational text from shopping chatbots and the labeling of inventory images for object detection and classification models, encompassing a vast array of diverse product SKUs. The application of labels to conversations would bolster the retailer's conversational AI models, leading to more natural interactions and increased customer satisfaction. Labelbox provided an end-to-end, in-app labeling workflow tailored to conversational AI needs. In terms of ROI, the company reported a 25% increase in data accuracy, along with a 25% reduction in turnaround time for model development.
Create bespoke NLP and NER experiences with powerful models. Generate ground truth for these models using our powerful text editor that supports classifications, entity recognition, relationships on raw text snippets or threaded conversation
Achieve up to 80% in labeling efficiency gains with model-assisted labeling and bulk classifications – use models to pre-label data, and let humans focus on corrective actions to generate ground truth so they don’t need to start from scratch.
Use dynamic filters to curate vast amounts of unstructured text data, metadata, or text embeddings. Automatically add a label on matching results at scale and queue them for human feedback and review.
Access the world’s best data labeling teams to label your data on demand, at scale. We offer support in numerous domains, including content moderation in over 20 languages.
Easily search for text data using filters such as annotation, metadata, and similarity embeddings to prioritize text snippets to label or create review tasks to fix issues that matter the most.