What is Super Annotate
SuperAnnotate is an enterprise software platform designed for streamlining AI data annotation and evaluation workflows. It helps build feedback-driven annotation and evaluation pipelines to create and manage high quality AI data faster than ever for infinite use cases. It provides leading enterprise software for creating and managing large-scale multimodal AI datasets.
How to use Super Annotate
Using SuperAnnotate involves several steps:
- Build UI: Create a custom editor with drag-and-drop in Builder or start from a template, tailored to your use case.
- Add data: Connect your data storage and import large-scale datasets with just a few clicks.
- Customize workflow: Define annotation stages, add automated review layers, or integrate custom code with Orchestra to automate repetitive tasks.
- Manage project: Oversee teams and vendors side-by-side, track performance, compare costs, and ensure quality standardization.
- Curate and review: Annotate, comment, and refine datasets in an intuitive worksheet for streamlined quality control.
- Export and train: Export your final dataset to training platforms or downstream systems.
Features of Super Annotate
- Single Collaboration Hub to bring in external vendors or in-house teams.
- Performance Analytics to compare performance across vendors.
- Simplified Procurement to onboard new service providers instantly.
- Customizable annotation editor (Builder).
- Large-scale dataset import capabilities.
- Workflow orchestration for defining stages, reviews, and automation (Orchestrate).
- Project management tools for tracking progress, performance, and resource allocation.
- Intuitive interface for curating and reviewing datasets (Explore).
- Data export functionality.
- Integration with data sources, model training pipelines, and other tools.
- Enterprise-grade security and compliance, including SOC 2 Type II, ISO/IEC 27001:2022, GDPR, CCPA, HIPAA, SSO, and 2FA.
Use Cases of Super Annotate
SuperAnnotate is built for various cutting-edge AI initiatives and data types, including:
- RLHF (Reinforcement Learning from Human Feedback) for building large-scale preference datasets.
- SFT (Supervised Fine-Tuning) for creating fine-tuning datasets.
- Agents for reviewing agent choices.
- RAG (Retrieval Augmented Generation) for ensuring system performance.
- Evaluation for understanding models in depth.
- Handling Multimodal, Image, Video, NLP, and Audio data.
- Supporting industries and use cases like LLMs & GenAI, Agriculture, Healthcare, Insurance, Sports, Autonomous driving, Robotics, Aerial imagery, NLP, and Security and surveillance.
- Benefiting personas such as Executives, ML Researchers, Data Engineers, Data Team Leads, Data Trainers, and Vendor & Procurement Managers.
