Surge AI provides a data labeling platform that utilizes human feedback to enhance the training of large language models (LLMs). By delivering high-quality labeled data, Surge AI enables organizations to improve the accuracy and performance of their NLP applications.
Funding
$25M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Large language models (LLMs) require vast amounts of labeled data to achieve optimal performance, but obtaining high-quality, human-labeled data is a significant bottleneck. Existing data labeling solutions often lack the domain expertise and quality control mechanisms necessary to meet the specific needs of LLM training.
Solution
Surge AI provides a data labeling platform that leverages a global workforce and proprietary technology to deliver high-quality, human-labeled data for training and fine-tuning LLMs. The platform specializes in complex NLP tasks, offering custom labeling teams with domain expertise, advanced quality control measures, and rapid experimentation interfaces. By providing access to skilled labelers and a robust platform, Surge AI enables organizations to improve the accuracy, safety, and capabilities of their AI models.
Target Audience
Surge AI primarily serves AI labs, enterprises, startups, and researchers who are developing and training large language models and other NLP applications.
Features
- Custom labeling teams with expertise in various domains, including STEM, finance, and law
- Proprietary quality control technology that combines human oversight and AI algorithms
- Rapid experimentation interface for designing and launching new labeling jobs
- Red teaming tools for identifying and mitigating potential safety risks in LLMs
- API and SDK for seamless integration with existing machine learning workflows
- Support for various data labeling tasks, including reinforcement learning with human feedback (RLHF), supervised fine-tuning (SFT), and content moderation
- Access to pre-built datasets, including the world's largest social media toxicity dataset