Labelbox Inc.
Labelbox empowers the world's top AI innovators with expertise and tools to create, manage, and scale the ultimate data factory for groundbreaking AI solutions. The company provides integrated solutions for training data including an enterprise platform, frontier data labeling services, and an expert marketplace.
At a Glance
- AI labs and research organizations
- Enterprise AI teams
- Fortune 500 companies
- Healthcare and life sciences
- +9 more
AI Tools by Labelbox Inc.
(1)Alignerr
AI Training Contractor Platform
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Latest News
Cognitive Revolution podcast: The data factory - Inside the $100B race for post-training supremacy, with Labelbox CEO Manu Sharma
This Week in Startups: Interview with Manu Sharma on Labelbox's hybrid data labeling approach
AI + a16z podcast: AI's unsung hero - data labeling and expert evals
Labelbox launches Alignerr Connect marketplace, Multimodal Reasoning Leaderboard, and expanded evaluation capabilities
Products & Services
Enterprise platform and tools for training data creation and management. Includes core catalog, annotation, and model features with data curation, natural language search, model-assisted labeling, and workflow automation.
Comprehensive annotation tools supporting image, video, audio, text, PDF, and multimodal data annotation with advanced features like SAM2 auto-segmentation and nested classifications.
Data management system with natural language search, similarity search (supporting up to 1 billion data rows), smart select, cluster view, and cloud bucket sync for AWS, Google Cloud, and Azure.
Model experimentation and evaluation platform supporting prediction, comparison, and fine-tuning of models. Includes access to GPT, Claude, Gemini, and other frontier models for annotation automation and evaluation.
Market Position
Labelbox competes in the AI training data and data labeling platform market alongside Scale AI, Snorkel AI, SuperAnnotate, and Encord. The company differentiates itself through its comprehensive data-centric AI platform that combines software, services, and an expert marketplace. Key competitive advantages include: (1) Hybrid approach combining software platform with managed services and expert network of 1M+ knowledge workers; (2) Focus on frontier AI and complex post-training tasks including RLHF and model evaluation; (3) Deep integration with cloud providers (AWS, Google Cloud, Azure) and frontier AI models; (4) Enterprise-grade security and scalability; (5) Specialized capabilities for multimodal AI, robotics, and embodied intelligence; (6) Strong backing from leading AI investors including SoftBank, Andreessen Horowitz, and Databricks.
Leadership
Founders
Manu Sharma
Before co-founding Labelbox in 2018, Manu Sharma worked at Planet Labs (geospatial satellite imaging company), served as Product Manager and Drone Engineer at DroneDeploy, founded Ardulab/Infinity Aerospace as CEO, and interned at Dubai Airports. He studied aerospace engineering and attended Singularity University Graduate Studies Program in 2012. He identified data labeling challenges during his aerospace engineering work.
Brian Rieger
Worked in the aeronautics industry designing and testing flight control systems and experimenting with machine learning models. Studied aerospace engineering and identified data labeling challenges during his work, which led to co-founding Labelbox. Currently serves as Chief Product Officer & Co-Founder.
Dan Rasmuson
Studied aerospace engineering and identified data labeling challenges during his studies and work in the field. Co-founded Labelbox in 2018 and currently serves as CTO & Co-Founder.
Executive Team
Manu Sharma
CEO & Co-Founder
Former Product Manager at Planet Labs and DroneDeploy, founder of Ardulab/Infinity Aerospace, studied aerospace engineering, attended Singularity University 2012
Brian Rieger
Chief Product Officer & Co-Founder
Worked in aeronautics industry on flight control systems and ML models, studied aerospace engineering
Board of Directors
Founding Story
The founders—Manu Sharma, Brian Rieger, and Dan Rasmuson—identified a critical bottleneck in AI development while working in aerospace engineering. They recognized that deep learning was becoming a major force in technology, particularly in self-driving cars, but would eventually power many other facets of life. They realized that the only way to create these AI models was through human-labeled data, which presented significant challenges in terms of difficulty, delays, and false starts. The founders initially built Labelbox on nights and weekends as their third business together, launching it on Reddit to gain early traction. The company emerged from six months in stealth mode in July 2018 with $3.9 million in seed funding.
Business Model
Revenue Model
Hybrid model consisting of software subscriptions and service fees. Software revenue is driven by a free-to-paid subscription tier system with Labelbox Units (LBUs) and workspaces available as purchasable add-ons. Services are billed based on volume or project-specific needs, with volume discounts available at scale. Alignerr Connect utilizes a per-project staffing model for direct hiring of experts.
Pricing Tiers
Ideal for individuals or small teams. Includes up to 30 users, 50 projects, 25 ontologies, and 1 workspace. Provides core catalog, annotation, and model features with data curation, natural language search, model-assisted labeling, self-serve labeling service add-ons, and community support.
Available for students and teachers at qualified educational institutions for non-commercial research.
Ideal for enterprises and AI teams. Includes unlimited users, projects, and ontologies. Features include Labelbox Monitor, SSO, custom embeddings, Foundry models, multimodal chat editor, auto-labeling tools, frontier and custom models, AI critics, labeling quality guarantees, proactive platform alerts, and premium support. Add-ons available for additional LBUs, workspaces, and Security/HIPAA compliance.
On-demand labelers for standard CV, NLP, and multilingual projects.
Specialized AI trainers for complex post-training and evaluation projects with access to 1M+ knowledge workers including 50K+ PhDs.
Direct hiring of experts on a per-project basis to staff in-house operations.
Target Markets
- AI labs and research organizations
- Enterprise AI teams
- Fortune 500 companies
- Healthcare and life sciences
- Autonomous vehicles and transportation
- Retail and e-commerce
- Post-training and reinforcement learning from human feedback (RLHF)
- Model evaluation and benchmarking
- Computer vision applications
- Natural language processing and LLM development
- Generative AI and multimodal AI development
- Robotics and embodied intelligence
- Google Cloud
- Speak
- Dialpad
- Ancestry