SuperAnnotate AI, Inc.
SuperAnnotate provides AI data infrastructure for creating, evaluating, and improving training-ready datasets across modalities. Its platform combines annotation, data curation, workflow orchestration, automated and human-in-the-loop quality assurance, model evaluation, and a managed expert workforce to help enterprises and frontier AI teams build reliable models faster.
At a Glance
- Frontier AI labs and foundation-model developers
- Enterprise AI and machine-learning teams
- Autonomous vehicles and robotics
- Healthcare and medical imaging
- +3 more
AI Tools by SuperAnnotate AI, Inc.
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SuperAnnotate
AI Training Data Annotation Platform
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Latest News
Launched the SuperAnnotate MCP Server, enabling AI agents to work directly with projects, folders, and items.
Announced expanded NVIDIA enterprise AI efforts, including connected workflows using NVIDIA NeMo microservices and SuperAnnotate human-in-the-loop data operations.
Announced the SuperAnnotate and Amazon SageMaker Partner Incubator integration and AWS Marketplace availability.
Released Agent Hub, connecting Fireworks AI, AWS Bedrock, Google Vertex AI, and Databricks models directly to annotation and evaluation workflows.
Products & Services
Multimodal platform for creating, curating, annotating, quality-checking, orchestrating, and evaluating training data for image, video, audio, text, code, LiDAR, RLHF/SFT, and agentic workflows.
Tools and managed workflows for building high-quality datasets used to fine-tune LLMs and other generative AI models.
Connects models hosted on Fireworks AI, AWS Bedrock, Google Vertex AI, or Databricks directly to annotation and evaluation workflows for pre-labeling, QA, and model-assisted review.
Model Context Protocol server that lets AI agents interact directly with SuperAnnotate projects, folders, and items without custom integration glue code.
Market Position
SuperAnnotate positions itself as an end-to-end AI data infrastructure and DataOps platform rather than a single-purpose labeling tool: it combines multimodal annotation, data management, workflow orchestration, human experts, automated quality gates, model evaluation, and agentic workflows. Its stated competitive set includes Scale AI, Labelbox, and Dataloop; reporting on its 2025 funding also frames it as competing with Scale AI and Surge AI. Differentiators emphasized by the company are configurable workflows, AI-and-human collaboration, enterprise integrations, and managed domain experts.
Leadership
Founders
Vahan Petrosyan
CEO and co-founder; he and his brother left PhD studies to commercialize research in image segmentation. His prior doctoral research at KTH Royal Institute of Technology focused on image segmentation.
Tigran Petrosyan
Co-founder and president; Vahan's brother, who left PhD research in biomedical imaging and photonics to start SuperAnnotate. His LinkedIn profile lists ETH Zurich.
Davit Badalyan
CTO and co-founder; technology leader with an engineering background, including education at the State Engineering University of Armenia.
Jason Liang
CBO and co-founder; leads business development and commercial activities at SuperAnnotate.
Executive Team
Vahan Petrosyan
CEO & Co-founder
Co-founder whose image-segmentation PhD research formed the basis for the company.
Davit Badalyan
CTO & Co-founder
Co-founder responsible for technology and platform development; has an engineering background.
Founding Story
The company began when Vahan and Tigran Petrosyan recognized that Vahan's PhD work in image segmentation could help companies replace tedious, pixel-precise computer-vision annotation work with a specialized platform. They initially focused on image annotation, then expanded into multimodal enterprise AI data operations as generative AI created demand for complex, high-quality training and evaluation data.
Business Model
Revenue Model
Enterprise software revenue from platform plans and custom enterprise contracts, complemented by paid managed annotation, evaluation, and AI DataOps services. The company sells through direct sales and cloud marketplaces including AWS and Google Cloud.
Pricing Tiers
For small projects; includes the multimodal editor, data curation/exploration, analytics, team and project management, 1,000 compute hours, and onboarding.
For scaling AI projects and MLOps; includes 2,500 compute hours, SSO, a dedicated Slack channel, and a dedicated customer success manager. Pricing is handled through a demo request.
For established, recurring, high-volume projects; includes 10,000 compute hours, advanced analytics, SSO, dedicated support, a solutions engineer, and AI DataOps consulting. Pricing is handled through sales.
Target Markets
- Frontier AI labs and foundation-model developers
- Enterprise AI and machine-learning teams
- Autonomous vehicles and robotics
- Healthcare and medical imaging
- Manufacturing, industrial inspection, and supply chain
- Agriculture and geospatial applications
- Foundation-model pre-training and post-training data
- LLM fine-tuning, supervised fine-tuning, and RLHF preference data
- RAG dataset creation and evaluation
- AI-agent trajectory, tool-use, and long-horizon evaluation
- Human and automated model evaluation and red teaming
- Computer-vision annotation for autonomous vehicles, robotics, industrial inspection, agriculture, and medical imaging
- Databricks and its Mosaic AI team
- ServiceNow
- NVIDIA
- Motorola Solutions