Reinvent the framework
of existing data-centric AI platforms


Meet next generation intelligence-centric platform – STADLE


Key Components

Federated & Transfer Learning

User-friendly GUI & APIs

User AI Model Database


The Economy Impact

Key Result



Data Privacy


Power Consumption


In Transferred Data Low Latency

Enabling New Business

Privacy-preserving AI is the only way to create AI diagnosis business in Medical Record Learning

Personal home robots need privacy-preserving AI to learn and improve capabilities

Improve Efficiency:
Self-Driving Car

Before: Upload 1GB per second, 10 TB per day

After: Upload only AI models 500MB per hour, 1.5 GB per day

About 6000 times efficient than current cloud-based solution

Saving on Cloud Data Center Cost

Investment: $200K STADLE License vs. $1M for 1,000 sq. ft. datacenter

Return: Annually save $800K on datacenter + network only for 1,000 sq. ft.

Enable New Business, Improve Efficiency, and Save Money

Technologies in Place


Federated Learning

Federated Learning (FL) has gained worldwide recognition after Google Research released a mobile application where all the training happens at mobile devices of users. The private data of users will not leave from distributed devices, and the local AI models are aggregated to provide collective intelligence. The cost to maintain big data is significantly reduced by FL, while the level of intelligence is not compromised. FL can be applied not only to mobile services but also to all services where customers’ privacy comes into the picture. TieSet has succeeded in developing the world’s first fully decentralized federated learning technology.


Privacy-Preserving AI

With the rapid growth of Artificial Intelligence technologies, concerns over consumer privacy have been increased to a large extent, especially in areas such as healthcare, home appliances, private pictures, and videos that user’s privacy is essential. To address privacy concerns, privacy communities must bring their knowledge to the machine learning field. Many privacy-enhancing techniques concentrated on allowing multiple users to collaboratively train ML models without exchanging local data. TieSet will lead the area of distributed AI so that a wide variety of people could benefit from the true power of AI.


Continuous Learning

Artificial Intelligence models have been designed and created in a static way in big data systems. However, intelligence is not a product of single-shot learning but needs to continuously grow with dynamic environment.


STADLE assists users to create dynamic distributed learning environments where the constant change and trend of data and behaviors can be absorbed with collaborative training processes.


Transfer Learning

When data is limited, Transfer Learning (TL) aims at improving performance in the accuracy or training time of an AI model in a target domain by using knowledge contained in a different but related source domain.


With TL we can deploy your AI solution faster and more efficiently by reusing previously generated models. Additionally, a system can learn a set of completely new tasks from the combination of previously acquired models by using a proprietary model synthesis engine.

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