Enabled Intelligence provides secure data labeling services with expert human annotators to ensure high-quality, accurate datasets for AI model training. Their solutions address the critical need for reliable data in mission-sensitive applications, enhancing model performance and reducing bias.
Funding
$1M 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
Many organizations struggle to create high-quality, accurate datasets for training AI models, especially when dealing with sensitive or classified data. Inaccurate or biased data can lead to poor model performance and unreliable results, particularly in mission-critical applications.
Solution
Enabled Intelligence provides secure data labeling services, leveraging expert human annotators and best-of-breed technology to ensure high-quality datasets for AI model training. They offer a range of services including data curation, AI model development, data labeling, AI model testing, feature extraction, and LLM fine-tuning and testing. Their focus on AI data quality helps customers with high-consequence missions and applications gain actionable insights faster, enhancing model performance and reducing bias.
Target Audience
Enabled Intelligence primarily serves organizations with high-consequence missions and applications that require accurate and reliable AI models.
Features
- Data Curation: Collection, structuring, indexing, and cataloging of AI data.
- AI Model Development: Rapid development and deployment of high-performance AI models.
- Data Labeling: Annotation of sensitive and classified data, including images, text, speech, audio, and video.
- eView™ Pre-Labeled Datasets: A library of pre-labeled datasets for training and testing AI models.
- AI Model Testing: Rigorous evaluation methodologies to identify issues related to bias, precision, recall, and accuracy.
- Feature Extraction: Precise location and extraction of features in various data formats.
- LLM Fine-Tuning and Testing: Elimination of hallucinations and improvement of LLM accuracy, relevance, and contextual understanding.