TieSet

Based in Silicon Valley, TieSet Inc. is an AI orchestration company established in 2020. The firm was founded by CEO Kiyoshi Nakayama and Co-Founder & CTO George Jeno. Dr. Nakayama, who holds a PhD in Computer Science from the University of California, Irvine, previously worked as a research scientist at NEC Laboratories America and is the primary author of "Federated Learning with Python." Mr. Jeno, a Georgia Tech alumnus with a Master's in Computer Science, provides the deep knowledge in machine learning theory and system architecture that underpins the company's technology. Sri Rao, a former product leader at Samsung and Meta, contributes as a synthetic founder and Board Member, guiding strategic vision.

TieSet operates in the enterprise AI market, providing solutions that address fundamental issues of data privacy, scalability, and computational costs for industries like insurance, manufacturing, and healthcare. The company's core offering is STADLE (Scalable Traceable Adaptive Distributed Learning), an intelligence orchestration platform. STADLE utilizes distributed and federated learning, enabling enterprises to train AI models on decentralized data without needing to move the raw data to a central server. This approach enhances data security and privacy while allowing for continuous and collaborative model training. Key features of the platform include model management, validation, distribution, and aggregation, which allows the system to integrate learnings from multiple environments to maintain a high-performing AI model.

The business model centers on providing STADLE through different service offerings. Clients can opt for a STADLE SaaS service, using TieSet's cloud platform via APIs, or choose an on-premise software license to run STADLE on their private cloud infrastructure like AWS or Azure. TieSet also offers integration and monitoring support, joint research and development partnerships, and consulting services for AI projects. This model allows enterprises to develop and deploy AI models more efficiently, reportedly reducing costs and accelerating the time to production. By not centralizing data, the platform mitigates risks of data breaches and reduces the strain on network resources.

Keywords: AI orchestration, federated learning, distributed machine learning, enterprise AI, model management, data privacy, MLOps, edge AI, continuous learning, collaborative training