Focusing on Full-Stack Large Model Core Technologies

I use xDAN-Agent for financial analysis to generate risk assessment reports. Its logical reasoning is superior, more accurate than GPT-3.5, and handles 200k context smoothly. I'm amazed by the strength of domestic large models and highly recommend everyone to try it!

We use xDAN-Agent for customized family financial planning. The knowledge graph accurately analyzes income and expenses, generating financial health reports 3x faster than manual work. Amazing professionalism - even beginners can understand professional analysis. Recommend every family to deploy it!

Using xDAN-Agent for civil dispute resolution, combined with knowledge graphs to enhance decision-making efficiency, significantly reducing manpower and time costs. Impressed by its performance in vertical domains, especially in legal retrieval, evidence organization, and risk assessment.

In education, we use xDAN-Agent for personalized teaching with a complete teaching-research-learning loop, and student performance has improved significantly. Current AI teaching tools can achieve such high efficiency - we're very satisfied and recommend everyone to try it.

Using the intelligent document assistant in government document processing scenarios - fast document recognition, high information extraction accuracy, guaranteed data security, and manual processing time reduced by half. Strongly recommend all government units to deploy it quickly.

Using the intelligent assistant in daily government work - document processing efficiency has soared significantly, repetitive tasks greatly reduced. It supports batch document classification, intelligent policy content extraction, and automatic formatting, greatly reducing manual burden.

When using the research search function to assist in writing papers, the system efficiently handles academic reports of over 10,000 words, demonstrating strong associative reasoning and logical organization abilities. Overall writing efficiency improved by at least 8x, greatly reducing research burden.

In civil dispute mediation using the mediation assistant, the system has 4 intelligent agents working collaboratively with clear division of labor and professional efficiency, covering key aspects like fact sorting, legal analysis, and emotional guidance. The overall mediation process is clear and smooth with fair judgment and objective expression.

I use xDAN-Agent for financial analysis to generate risk assessment reports. Its logical reasoning is superior, more accurate than GPT-3.5, and handles 200k context smoothly. I'm amazed by the strength of domestic large models and highly recommend everyone to try it!

We use xDAN-Agent for customized family financial planning. The knowledge graph accurately analyzes income and expenses, generating financial health reports 3x faster than manual work. Amazing professionalism - even beginners can understand professional analysis. Recommend every family to deploy it!

Using xDAN-Agent for civil dispute resolution, combined with knowledge graphs to enhance decision-making efficiency, significantly reducing manpower and time costs. Impressed by its performance in vertical domains, especially in legal retrieval, evidence organization, and risk assessment.

In education, we use xDAN-Agent for personalized teaching with a complete teaching-research-learning loop, and student performance has improved significantly. Current AI teaching tools can achieve such high efficiency - we're very satisfied and recommend everyone to try it.

Using the intelligent document assistant in government document processing scenarios - fast document recognition, high information extraction accuracy, guaranteed data security, and manual processing time reduced by half. Strongly recommend all government units to deploy it quickly.

Using the intelligent assistant in daily government work - document processing efficiency has soared significantly, repetitive tasks greatly reduced. It supports batch document classification, intelligent policy content extraction, and automatic formatting, greatly reducing manual burden.

When using the research search function to assist in writing papers, the system efficiently handles academic reports of over 10,000 words, demonstrating strong associative reasoning and logical organization abilities. Overall writing efficiency improved by at least 8x, greatly reducing research burden.

In civil dispute mediation using the mediation assistant, the system has 4 intelligent agents working collaboratively with clear division of labor and professional efficiency, covering key aspects like fact sorting, legal analysis, and emotional guidance. The overall mediation process is clear and smooth with fair judgment and objective expression.