nTieSet offers STADLE, a platform that optimizes the development, deployment, and updating of AI models by enabling secure training across data silos and reducing operational costs by up to 90%. The technology accelerates model updates by 10 times and enhances accuracy by 40%, making AI more accessible and efficient for enterprises in industries such as insurance, manufacturing, and healthcare.
Funding
$420K 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
Developing and deploying AI models at scale is costly, complex, and time-consuming, especially when dealing with distributed data silos and the need for frequent model updates. Traditional cloud-based solutions require significant computational resources, data transmission bandwidth, and specialized talent, leading to high operational expenses and slow iteration cycles.
Solution
TieSet offers STADLE, a platform designed to optimize the development, deployment, and updating of AI models across distributed environments. STADLE enables secure training across data silos, reducing operational costs by minimizing data transmission and computational overhead. The platform accelerates model updates and enhances accuracy by leveraging proprietary algorithms that efficiently integrate new data and optimize model performance at the edge and in the cloud. By simplifying model management and tracking performance, STADLE empowers machine learning teams to focus on growing their business and driving innovation.
Target Audience
The primary target audience includes machine learning teams in enterprises across industries such as insurance, finance, manufacturing, and healthcare, seeking to optimize their AI development and deployment processes.
Features
- Secure federated learning across distributed data silos, preserving data privacy and reducing data transmission costs.
- Optimized model training at the edge and in the cloud, leveraging available computational resources efficiently.
- Accelerated model updates through rapid integration of new data and efficient retraining algorithms.
- Simplified model deployment and management with centralized performance tracking and monitoring.
- Support for various AI model types and frameworks, ensuring compatibility with existing machine learning workflows.
- Reduction in training times by up to 33% and data transmission by up to 1000x in industrial and manufacturing applications.
- Fraud detection accuracy increase of 40% to 90% in insurance and finance applications while preserving data privacy.