SuperAnnotate AI Data Platform

SuperAnnotate was started in August 2018 by brothers Tigran and Vahan (CEO Vahan Petrosyan), who left their PhD programs, and moved its headquarters to Silicon Valley in January 2019 after joining Berkeley SkyDeck. It helps AI teams produce training and evaluation data for computer vision, language and agent models.

The platform combines a multimodal annotation editor for image, video, audio, LiDAR, text and RLHF preference data with a drag-and-drop custom UI builder. A model-agnostic hub connects GPT, Claude, Gemini, Llama, Mistral and Qwen to pre-label data, assist annotators and act as rubric judges. Further modules cover LLM arena comparisons, agent evaluation with custom verifiers, workforce and project management, quality gates and event-driven orchestration that routes items between AI and humans. A vetted expert network and managed operations are offered alongside the software.

• Multimodal editor: annotates image, video, audio, LiDAR, text, SFT and RLHF data in one tool.
• Custom UI builder: drag-and-drop forms, media players and custom code, with an agentic assistant to build layouts.
• Model hub: connects GPT, Claude, Gemini, Llama, Mistral, Qwen and fine-tuned models for pre-labeling and judging.
• LLM and agent evaluation: blind side-by-side arenas with Elo scores, rubric reviews and RL environments with custom verifiers.
• Quality and orchestration: quality gates, multi-stage workflows and event triggers that route items between AI and humans.
• Workforce management: role-based access for admins, QA leads, annotators and vendors, plus dashboards.

Vietnamese data labeling and BPO firms can run client projects for vision and LLM data on one platform with measurable quality gates. AI product companies building Vietnamese chatbots or assistants can run side-by-side evaluation and RLHF with local experts. Manufacturers and automotive suppliers can label image, video and LiDAR data for inspection and driver-assistance models.

Customer examples on the site cite 2x faster time to model (Twelve Labs), annotation time cut to one third (Körber) and a 10% higher F1 score (GumGum). Model pre-labeling and confidence-based auto-approval reduce manual work, while quality gates such as inter-annotator agreement and expert spot-checks keep datasets consistent. One workspace serves vision, LLM and agent data projects.

As foundation models become commodities, data quality and evaluation have become the main lever for model performance, and demand is shifting from simple bounding boxes to expert RLHF, agent evaluation and multimodal data. Southeast Asia, including Vietnam, hosts many data labeling teams and AI product companies that need tooling to manage quality and mixed human-AI workflows.

Founded in 2018; headquarters in Silicon Valley since January 2019. Raised USD 3 million seed (Point Nine, 2020), USD 14.5 million Series A (Base10, 2021) and a USD 50 million Series B led by Socium Ventures with NVIDIA, Databricks Ventures and Dell Technologies Capital (2024–2025). SOC 2 Type II and ISO 27001:2022 certified.