HumanSignal provides a data labeling platform that combines automation and human oversight to prepare training data, fine-tune large language models, and evaluate AI outputs. This solution enhances model accuracy and efficiency while ensuring compliance and data security across various use cases and data types.
Funding
$30.2M 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.
RFounders
Product
Problem
Building and maintaining high-quality training data for machine learning models is a complex and costly process, often requiring a combination of automated tools and human expertise. Ensuring data accuracy, consistency, and compliance across diverse data types and use cases presents a significant challenge for data science teams.
Solution
HumanSignal offers Label Studio Enterprise, a data labeling platform that unifies automation and human oversight to streamline the preparation of training data, fine-tune large language models (LLMs), and evaluate AI outputs. The platform provides customizable workflows and interfaces to support various data types, use cases, and machine learning backends. By integrating automated evaluation with human expert review, HumanSignal enables data science teams to improve model accuracy, accelerate development cycles, and maintain data security and compliance.
Target Audience
HumanSignal's primary customers are data science teams and organizations building and deploying machine learning models across various industries, including legal, medical, and retail.
Features
- Customizable interface for data labeling and evaluation, adaptable to specific project requirements
- Automated evaluation capabilities using AI to pre-screen data and reduce the workload on human labelers
- Human evaluation workflows for complex edge cases requiring nuanced expert review
- Integration with LLMs and other popular or custom models for automated data processing
- Robust API/SDK for seamless integration with existing ML/AI pipelines and workflow automation
- Role-based access control (RBAC) and single sign-on (SSO) for secure user management and permissions
- Queue management features to customize and automate labeling queues for efficient task assignment and review
- Agreement matrix to visually identify ground truth data items and potential data quality issues
- Dashboards for monitoring model and human labeling performance with actionable insights
- SOC2 certification and HIPAA compliance to ensure data privacy and security