Multi GPU acceleration
When running within a large-scale AI infrastructure, the distributed learning speed for generative AI full-tuning improves by an average of 1.5 times.
Jonathan®
Generative AI Platform for Everyone
Generative AI Orchestration Jonathan®
No/Low Code Provision
Provide a UI accessible to all users, including non-expert domain specialists.
End-to-End Solution
Seamlessly provide all the essential experiences for development, launch, and operation.
AI Model Optimization
Connect advanced AI models with the LLM foundation model to optimize performance.
Scalable Infrastructure
Provide all infrastructure environments required by enterprises, including SaaS and on-premises.
Overview
Jonathan is a generative AI platform that includes AI infrastructure management and workflow automation capabilities, efficiently automating all stages of AI development including data collection, preparation, training, and deployment, thereby maximizing development efficiency and productivity.
AI development
End-to-End solution
AI service plan
Prepare training data
AI model training
Learning performance verification
Deploy learning model
AI service operations
Technology
Jonathan is a platform that integrates core foundational technologies to build an economical and efficient AI infrastructure, providing the optimal environment for developing and deploying generative AI models.
Distributed learning
Efficiently manages computational load and memory requirements that arise when training artificial intelligence models
Federated learning
Developing an optimized artificial intelligence model without infringing on data ownership during group learning
Hyper parameter search
Automatically adjusts various hyperparameter values that affect model training
Key Features
Infra
Jonathan integrates multi-GPU acceleration and virtualization technologies to reduce the complexity of LLM development and build high-performance AI infrastructure at a low cost.
AI development management
- Creation of infinite workspaces for AI learning and GPU support/management
- Systematic support according to operational purpose
Resource and network management
- Real-time monitoring of GPU server operation status and network interface between servers
- Rapid failure detection and cause analysis
Building AI
- Pre-trained model library available
- Provides tools for constructing learning data
- Independent data storage for each team
Efficient development environment
- Applies GPU acceleration technology, integrated memory, and multi-node connection technology
- GPU allocation and automatic scheduling
Stable operating environment
- Adds one-click operating resources
- Automatic load balancing applied
- NVIDIA TensorRT applied
Models
With Jonathan, users can develop various generative AI-based models applicable to different industries. This helps democratize AI technology and implement innovative AI solutions across diverse fields.
Visual intelligence
Recognize, track and predict visual data such as images and videos.
Object recognition, object tracking, age/gender prediction
Empathetic intelligence
Recognize and classify human emotions in text, voice, and video.
Text, voice, video, multimodal empathy, etc.
Medical intelligence
Understand various medical data such as electronic medical records (EMR), clinical information, and images.
Diagnosis of thyroid nodules, etc.
Conversation generation intelligence
Generate valid responses to utterances to enable natural conversation.
Free conversation, creation of valid answers, etc.
Natural language understanding intelligence
Process human language so that artificial intelligence can learn it. Understand the meaning and characteristics of text.
Text embedding, intent analysis, age/gender prediction, etc.
Recommendation intelligence
Recommend content based on preference and similarity by learning meta information of users and content.
Movie/music recommendations, etc.
Data Analysis
Jonathan leverages generative AI to systematically analyze and manage data. It efficiently collects, analyzes, and visualizes dispersed data, supporting users in making optimal decisions, thus enhancing AI accessibility through fair technological innovation.
Extensive data collection
- • Real-time collection from multiple channels
- • Quick save/view/search
AI and statistical analysis
- • 34 types of emotion analysis
- • Inference of topic, age group, gender
- • Related keyword analysis
Data flow analysis
- • Buzz volume time series analysis
- • Provides quantitative demographic indicators
Annotation
Jonathan supports comprehensive annotations for text, images, and videos. It accurately organizes and structures diverse elements to generate high-quality training data, enabling the development of superior AI models applicable across various industries.
Annotation work environment
Workers and schedule management
- Workload/time/accuracy can be managed for each worker
- Efficient work distribution
Designed for learning purposes
- Freely customize annotation elements to suit your work purpose
Convenient working environment
- Easy and comfortable work environment based on GUI
- Data inspection and management in a single system
AI work assistance
- AI model recognizes objects, emotions, and intentions to assist with annotation work
Generate high-quality annotator data
Data quality management
- Data quality control with data cross-validation
Multimodal annotations
- Supports various annotations such as subject, object, and relationship on one data
Various media support
- Supports all formats of media including text, image, voice, and video
Fast and accurate results
- Secure large amounts of data with minimal work based on AI tagging
LLM
Jonathan features advanced capabilities such as RAG, Playground, and Finetuning, facilitating the effective development of sophisticated language models. These functions maximize the efficiency of LLM development and simplify the construction of generative AI models.
RAG System
Utilizes a vector database to help general LLM understand context and reduce hallucinations, retrieving more contextual information to provide accurate responses.
Playground
Test and compare multiple LLM using a single API without deployment, allowing users to select the optimal LLM and identify the most suitable model for specific purposes.
Finetuning
By using PEFT options, cost-effective model adjustment is possible, allowing users to customize LLM with their own data and achieve better performance.
① User-friendly UI
Supports no-code and low-code environment for easy use by non-experts.
② Versatile Infra Support
Compatible with SaaS and on-premise environments.
③ Performance Maximization
Enhances AI capabilities by integrating existing models with Jonathan's LLM foundation.