Use Labelbox to generate human preference datasets, integrate with fine-tuning APIs from leading model providers & hubs, and evaluate model performance. Optimize leading closed and open source Large Language Models (LLMs)
Empower human experts to validate model outcomes with advanced capabilities for benchmarking, ranking, selection, NER, and classification.
Evaluate pre-recorded and live chats with support for multi-turn conversations.
Ensure helpful, trustworthy, safe outputs with highly accurate datasets for instruction tuning, RLHF, and supervised fine-tuning.
Curate, annotate, and ready datasets with the best of human and AI-assisted data preparation.
Zero in on feedback and cases that matter with powerful curation and discovery capabilities. Support for native vector search and similarity searches.
Access leading models from OpenAI, Cohere, and Anthropic, as well as top open source models, from within the platform for streamlined fine-tuning flows.
Integrations with Vertex AI, Databricks, and other leading MLOps pipelines so you can launch fine-tuning jobs from within Labelbox.
Automate data labeling and augmentation tasks with Model Foundry. No-code data enrichment with leading closed-source and open-source LLMs at a fraction of the time and cost.
A collaborative human feedback platform to generate perfect data with internal experts and world’s most skillful data labeling services with expertise in RLHF, evaluation and red teaming.
A leading AI-powered customer intelligence platform company used fine tuning to build a powerful LLM over five years via five billion minutes of business conversations. The model offers out-of-the box capabilities to businesses to accurately summarize business calls, extract important insights and offer in-the-moment coaching to sellers and customer reps. Advanced discovery, curation, and annotation capabilities were crucial for building high-quality training datasets. Human evaluation was also critical to ensure quality outcomes before the system was turned live. Labelbox enabled the organization to accelerate the creation of the LLM by 75% through rich, integrated capabilities for data preparation and human evaluation.