
PRODUCTS & SERVICES
Adaptive-LLM – Adaptive Local Language Models
Adaptive LLM empowers businesses with locally hosted language models, ensuring security, customization, and efficiency for advanced natural language processing.

Local Processing
daptive LLM enables secure on-device language processing, safeguarding data privacy and minimizing external exposure.

Efficient Implementation
Quantization optimizes models for on-device performance, ensuring responsiveness even on low-end CPUs.

Cost-Effective Solution
By eliminating external API costs and leveraging local deployment, Adaptive LLM offers budget-friendly scalability and customization.

File Intelligence
Semantic file management with department tags ensures secure, smart file organization and controlled inter-departmental access.

Tailored Precision
Customizable vocabulary and tasks enhance precision, catering to user-specific needs and linguistic nuances.

STADLE – Scalable Traceable Adaptive Distributed Learning Platform
is the Next Generation Intelligence-Centric Platform solving the Most Essential Problems
in adopting AI

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

NEAR REAL-TIME INTELLIGENCE
With distributed and parallel learning process, not collecting big data, you can deliver intelligence before it gets outdated

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

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

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


The Solution
TieSet's solution revolutionizes language processing with Adaptive LLM, offering businesses the power of locally hosted models for unparalleled security, customized precision, and efficient performance. Our semantic file management and department tagging further enhance file organization and access control.
STADLE by TieSet offers an intelligent AI platform that addresses central data and AI system challenges. It harnesses shared user intelligence for privacy-conscious, efficient AI processes. In the realm of Adaptive-LLM, STADLE drives real-time AI applications, improves training efficiency, and reduces big data system costs, shaping the future of AI innovation with absolute privacy.

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, including Adaptive LLM, for healthcare, life-science, medical, and biotech systems. These systems analyze behavioral patterns and personal traits via Federated Learning, ensuring privacy by avoiding data transfers to centralized servers through the STADLE platform.
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For instance, in precision medicine, centralizing personal data for machine learning faces challenges. Health data movement conflicts with regulations like HIPAA and GDPR, while accuracy and personalization remain crucial.
​Our STADLE federated learning integrates with Health Data Platforms, Precision Medicine, Genomics, and Drug Discovery Software, enhancing predictive AI models' performance. STADLE empowers smart health devices, maintaining patient data privacy during training and human interactions. This versatile framework supports all smart device types.
USE CASE
Manufacturing
Learning Robotic manipulation from distributed robotic arm clusters using Federated Learning.
Efficient manufacturing hinges on diverse real-world data for tasks like grasping and manipulation. Current centralized approaches (e.g. Google Robotic Farm) are effective but slow and costly, hindering scalability (e.g. Amazon). By incorporating Adaptive LLM and Federated Learning, we revolutionize manufacturing processes, eliminating the need to sift through extensive manuals. Adaptive LLM can address complex manufacturing device queries effectively.
