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The data factory for AI teams

Labelbox delivers innovative services and software to operate, build, or staff your modern AI data factory

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Trusted by companies of all sizes — from startups to Fortune 500s

Latest work from Labelbox Research

Our applied research team is at the forefront of AI, developing novel methods for data generation and model evaluation. We partner with our customers to translate cutting-edge science into tangible breakthroughs, accelerating the development of robust and reliable AI systems.

Reinforcement learning with verifiable rewards (RLVR)

Unlocking the next level of AI utility with automated, verifiable feedback

Agentic trajectories

Refining the right data to train and evaluate agents effectively

Rubric evals

Fueling structured and standardized assessments of model performance

Data factory for Frontier AI

AI teams depend on a powerful AI data factory to generate unique training data and evaluate models. Labelbox is the only vendor with a comprehensive set of data solutions that can help you build, operate, or staff your custom data factory.

Services
Frontier data

Labelbox provides a fully managed solution for on-demand, high-quality labeled data and human evaluations, powered by our exclusive network of Alignerrs.

Discover labeling services
Software
Platform & tools

For companies seeking full control over data labeling operations. Harness our best-in-class software to evaluate models, enhance data and generate high-quality data faster.

Explore the platform
Staff
Hire proven AI trainers

Labelbox Alignerr Connect helps you discover and hire experienced AI trainers directly. Available to integrate seamlessly into your existing processes and tools.

Build your AI team
Achieve AI breakthroughs with the most innovative post-training alignment

Data for reinforcement learning

RLVR (Reinforcement learning from verifiable rewards)

Providing clean, automatic reward signals for tasks like math, code, or form completion where correctness can be programmatically verified.

Rubric-based evals

Enabling fine-grained feedback on subjective tasks by scoring outputs against human-defined criteria like clarity or helpfulness.

Solvers and verifiers

Delivering automated checks to solve or validate complex, multi-step outputs for higher-quality supervision.

RLVR (Reinforcement learning from verifiable rewards)

Accelerating AGI breakthroughs with frontier data

Complex reasoning

Complex reasoning

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Multimodal reasoning

Multimodal reasoning

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Multilingual

Multilingual

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See all use cases

Interested in task-specific labeling? Explore Computer Vision and Natural Language Processing (NLP).

Discover how top models perform with Labelbox Leaderboards

We bring precision to subjectivity. Enabling expert evaluations that reveal the blind spots of leading AI models across diverse topics.

Complex reasoning
Complex reasoning
Last updated: July 10, 2025
Agentic Search
Agentic Search
Last updated: June 13, 2025
Multimodal-reasoning
Multimodal-reasoning
Last updated: March 12, 2025

Fueling advancements in academia and research

Labelbox is behind the scenes of advanced AI research, driving innovation showcased at leading conferences such as CVPR, NeurIPS, and more.

Annotated Datasets for Trajectories’ Prediction: A Research Agenda

Claudia Greco, Giovanni Di Gennaro, Marialucia Cuciniello, Terry Amorese, Maria Santina Ler, Gennaro Cordasco, Amedeo Buonanno, Francesco A. N. Palmieri & Anna Esposito 

Abstract

The paper presents the research agenda of the Socially-Aware Learning through Interactions in Crowded Environments (SALICE) project. The ambition of the...

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A Benchmark for Long-Form Medical Question Answering

Pedram Hosseini, Bing Ren, Ali Farahanchi, Jessica M. Sin, Bryceton G. Thomas, Saeed Hassanpour, Elnaz Nouri

Abstract

There is a lack of benchmarks for evaluating large language models (LLMs) in long-form medical question answering (QA). Most existing medical QA evaluation benchmark

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TRINS: Towards Multimodal Language Models that Can Read

Ruiyi Zhang, Yanzhe Zhang, Jian Chen, Yufan Zhou, Jiuxiang Gu, Changyou Chen, Tong Sun

Abstract

Large multimodal language models have shown remarkable proficiency in understanding and editing images. However a majority of these visually-tuned models

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Why thought leaders choose Labelbox

quote

Our document intelligence teams are seeing a 2X increase in data quality.”

Our document intelligence teams are seeing a 2X increase in data quality.”
quote

Model accuracy improved 35% after using Labelbox, a 2x increase in model development.”

Model accuracy improved 35% after using Labelbox, a 2x increase in model development.”
quote

We now spend 2-3x less for quality data due to reduction in wasted spend.”

We now spend 2-3x less for quality data due to reduction in wasted spend.”

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