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Analysing data

Orchestrating

Collective Intelligence

Today, centralized data & AI systems face enormous challenges of privacy, scalability, and training efficiency taking longer time to create real insight from data.

Check out the Federated Learning book authored by the TieSet co-founders! 

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PRODUCTS & SERVICES

STADLE – Scalable Traceable Adaptive Distributed Learning Platform

is the Next Generation Intelligence-Centric Platform solving the Most Essential Problems
in adopting AI

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DATA PRIVACY-PRESERVING

Data remains at local user devices while providing more access to various data silos and sources through federated learning

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NEAR REAL-TIME INTELLIGENCE 

With distributed and parallel learning process, not collecting big data, you can deliver intelligence before it gets outdated

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BOOST REVENUE 

Build new product features and grow business with more customer data to fuel the most adaptive and personalized experience

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SCALABLE

With our unique horizontal scaling framework, you can connect unlimited devices such as more than 10K devices in minutes

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LOW-LATENCY

Edge AI training easily combined with STADLE's federated learning will realize low latency and cost efficient production of AI applications

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The Solution

TieSet Inc. offers an intelligence-centric AI platform called STADLE with continuous, distributed & collaborative learning frameworks that resolve the most essential problems in centralized data & AI systems such as privacy, learning inefficiency, latency, and high costs of utilizing huge data storages and computation resources. STADLE solves those problems by only gathering and aggregating the shared intelligence from users. 

Our customers significantly benefit from the STADLE platform in many ways to realize AI-empowered applications delivered in near real time. They can also enable new business areas where privacy has been a very serious bottleneck, drastic improvement is required for AI training process efficiencies, and/or management & maintenance costs of big data systems need to be reduced.

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Want to achieve 100% data privacy, deliver your AI solution much faster, access more data and save costs?

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USE CASE

Healthcare, Medical & Bio

No one wants to share their healthcare data, but everyone wants to know their health diagnosis in detail.

TieSet provides AI solutions and applications for healthcare, life-science, medical, and biotech systems that analyze behavioral patterns and personal traits without transfers of personal health data to a centralized server utilizing the Federated Learning framework provided by the STADLE platform.

 

For example, in the field of precision medicine, machine learning and analytics has been defendant on centralizing the personal data. However, moving health data is not a practical option also because of regulatory compliance such as HIPAA and GDPR, while maximizing the accuracy and personalization is a strong requirement. 

Our STADLE federated learning framework can be integrated into your Health Data Platforms, Precision Medicine Software, Genomics Software, and Drug Discovery Platforms to increase data accessibility from various data sources and silos to increase the performance of your predictive AI models. STADLE can be used in smart health devices to be trained and learn new tasks while keeping the patient’s data private and without posing a threat when interacting with humans. The framework is generalized to support all types of smart devices as well.

USE CASE

Manufacturing & Robotics

Learning Robotic manipulation from distributed robotic arm clusters using Federated Learning.

Grasping and manipulation in robots require extensive data from the real world of objects with different shapes and sizes and from various environments. In the current approach, robotic arms acquire grasping data in a single facility to train a centralized model. (e.g. Google Robotic Farm) While this approach leads to good results, acquiring data is time consuming and expensive resulting in a non-scalable process for a single company (e.g. Amazon), so this framework does not make general manipulators better. We propose to use Federated Learning to decentralized the learning of such robots.

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Our

Partners

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