Labelbox provides a comprehensive RL data engine and enterprise platform designed to build, evaluate, and continuously improve specialist AI agents. By integrating environments, evaluation, and training, the platform enables organizations to transform internal domain expertise and workflows into compounding intelligence. Unlike general-purpose models, Labelbox focuses on creating specialist models that excel at specific enterprise tasks, such as finance analysis, software engineering, and robotic manipulation, resulting in higher reliability and efficiency at lower token costs. The company partners with leading AI labs to solve complex data requirements and build high-quality benchmarks that remain discriminative even as model capabilities evolve. Some of the key features are:

• Recursion Platform: A unified RL platform that links enterprise data, simulation environments, and reinforcement learning training loops for specialist models.
• Horizon RL Environments: Scalable simulation platforms that generate realistic scenarios for training models in reasoning, tool use, and computer-based tasks.
• Terra Robotics Data: An end-to-end data pipeline for robotics foundation models, including high-quality video, teleoperation, and multimodal annotations.
• Alignerr Expert Network: A global network of over 2.6 million credentialed contributors across 200 domains who provide expert human judgment for training and evaluation.
• Calibration & Rubrics: Advanced evaluation systems that utilize expert-authored rubrics and continuous item response theory calibration to measure model performance accurately.
• Enterprise Governance: Built-in privacy, security, and compliance protocols including SOC 2 Type II certification to handle sensitive internal enterprise data.

Labelbox functions by helping organizations define their high-value workflows, connect these workflows to simulation environments, and evaluate the performance of models using expert-driven rubrics. The platform then uses these evaluations to create high-quality training signals, such as preference data and ranked trajectories, to feed into a continuous reinforcement learning loop. By treating production environments as training surfaces, models are continuously refined based on real-world execution signals, ensuring they adapt to edge cases and specific business requirements. Some common use cases include:

• Financial Analysis: Developing specialist agents for private equity and investment workflows that automate complex multi-step analysis tasks with higher accuracy than general models.
• Customer Support: Engineering service agents that resolve tickets with fewer hallucinations and faster response times while maintaining lower operational costs.
• Robotic Foundation Models: Creating training datasets for physical robots to generalize across diverse manipulation tasks, including picking, packing, and surgical instrument tracking.
• AI Benchmarking: Partnering with research labs to develop custom evaluation benchmarks like the Grounded Integration Measure to track the reasoning capabilities of frontier models.
• Cybersecurity Defense: Building adversarial simulation environments to test agent resilience and defensive capabilities against emerging security threats.
• Autonomous Research: Supporting autonomous scientific discovery by training agents on long-horizon reasoning tasks that require multi-step hypothesis verification.