Kili Technology provides tailored data annotation and evaluation services for large language models, utilizing expert-led project management to streamline the data pipeline. This approach eliminates data bottlenecks, enabling companies to enhance model performance and accelerate AI project deployment.
Funding
$31.9M 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
Training large language models (LLMs) requires massive datasets of accurately annotated data, which can be a significant bottleneck for AI development. Ensuring the quality and consistency of this data is challenging, often requiring specialized expertise and manual effort. Traditional data annotation processes can be slow, expensive, and difficult to scale, hindering the rapid iteration and deployment of AI projects.
Solution
Kili Technology offers a managed data annotation and evaluation service specifically designed for LLMs, providing high-quality training data at scale. They streamline the data pipeline by leveraging expert-led project management, eliminating data bottlenecks and accelerating AI project deployment. Their services include tailored data annotation, custom workflow design, and comprehensive LLM performance comparisons. By providing access to qualified domain experts and scalable annotation teams, Kili Technology enables companies to improve model performance and adapt to evolving quality standards.
Target Audience
Kili Technology's primary customers are machine learning engineers and AI product teams building and deploying large language models across various industries.
Features
- Tailored data annotation services for LLMs, ensuring high accuracy and consistency
- Expert-led project management to design, customize, and streamline the data pipeline
- Scalable and diverse domain expert teams with industry knowledge and language skills
- Specialized LLM comparisons and evaluation reports with detailed performance analytics
- Customizable workflows and iterative guideline development to meet specific quality standards
- Quality monitoring features to ensure data accuracy and reliability