Cloopen AI's large model knowledge assistant has been comprehensively upgraded. Centered on "higher efficiency, better understanding of finance, and better experience", it introduces two core capabilities: real-time voice questioning and real-time voice retrieval. It deeply integrates voice interaction with financial private domain knowledge, enabling every question raised by employees to directly reach value.
Through the Cloopen AI large model knowledge assistant, business personnel can directly have a "conversation" with the enterprise knowledge base using natural language, without the need to manually input keywords or prompts. This not only improves the query efficiency but also enables the AI to better understand the context of the user's questions and provide more accurate answers.
Upgrade highlights: smoother real-time voice interaction
# 01
Real-time voice questioning: instant response, more efficient queries
In financial work scenarios, querying industry-specific knowledge is time-consuming and laborious. often disrupts one's train of thought.
After the upgrade of Cloopen AI's large model knowledge assistant, business personnel only need to ask questions by speaking. Whether it's complex financial policy interpretation or comparison analysis of financial products， the large model knowledge assistant can perform real-time voice recognition and problem handling, and provide answers in the form of voice quickly.
For example, when a financial manager has a conversation with a client via WeChat Enterprise， by asking a question quickly in voice， "Compare the differences between Fund A and Fund B"， the large model knowledge assistant immediately provides the comparison results in the form of voice and charts， significantly improving the query efficiency.
# 02
Real-time Voice Search: Semantic Penetration, Knowledge Instant Location
Financial enterprises have an enormous amount of and complex documents. Traditional search methods are inefficient.
For the massive internal documents of financial enterprises, such as policy documents, product contracts, research reports, etc., the Cloopen AI large model knowledge assistant, through cross-modal semantic search technology, converts voice instructions into vector queries and precisely locates key information from the enterprise's private domain knowledge base.
For example, when an employee asks, "The clauses regarding embedded investment in the 2025 banking supervision regulations are", the system can automatically parse the long document, locate the original text and extract the summary, and provide feedback in the form of voice and text, increasing the search efficiency by 40%.
# 03
Vertical Domain Knowledge Enhancement: Financial Scenarios, Deep Adaptation
The Cloopen AI large model knowledge assistant has deeply delved into the financial field, thoroughly understanding the business logic of finance, accurately grasping enterprise-specific terms, product information, financial expertise, financial policies and regulations, and so on.
When facing complex and diverse financial issues, the large model knowledge assistant can deeply mine the semantic of documents, understand the true needs of users, provide authoritative and reliable knowledge support for financial practitioners, and help enhance their professional level.
For example, when a user asks questions like "The impact of interest rate liberalization on bank profits", the large model knowledge assistant can accurately understand the financial concepts and logical relationships involved, and provide in-depth analysis and professional answers that meet the user's needs.
# 04
Personalized Experience: Multiple Voices, Free Switching
To meet the individualized needs of different users, the large model knowledge assistant is equipped with multiple voices. Users can switch between different voices according to their preferences and usage scenarios to act as their own personalized knowledge assistant.
Financial applications, voice-driven efficiency revolution
# 01
Internal Management: Intelligent Q&A Solves the Bottleneck of the Reimbursement Process
Business Pain Points: Thousands of employees in a certain bank frequently consult the headquarters' finance/HR departments about frequent issues such as reimbursement procedures, procurement systems, attendance rules, etc. The traditional manual response is inefficient, resulting in delays in business execution.
Solution: Currently, employees can directly use the OA system and click on the Cloopen AI large model knowledge assistant to make voice inquiries, which can quickly solve 85% of daily consultation problems.
# 02
Statistical Operations: Efficient Interpretation of Data Indicators
Business Pain Points: The statistical operation team of the bank, every day has to deal with thousands of business indicators within the bank and the rapid changing operational data in the operation department's daily reports, weekly reports, and monthly reports.
Even professional business personnel often find it difficult to interpret these complex internal indicators and standards, and need to rely on operation staff to explain them one by one painstakingly.
Solution:
The statistical team uploads relevant documents, and the bank personnel, through the authorization of the large model knowledge assistant, can have natural language conversations with the operational data, and obtain precise explanations, reports, and insights from various data records.
# 03
Marketing Growth: Voice-Driven Customer Conversion "Zero Delay"
Business Pain Points: The bank's enterprise WeChat operation team faces frequent customer inquiries. Due to the complex content of activity rules, the search is inefficient and the response is delayed, resulting in missed marketing opportunities.
Solution: Integrate the large model knowledge assistant into the WeChat system and upload a large amount of marketing activity explanations. The agent can ask questions through voice and obtain the activity rules in real time and automatically recommend related discounts, increasing the customer inquiry conversion rate by 30%.
# 04
Customer Service: Voice to Create "Unobtrusive" Ultimate Experience
Business Pain Point: A certain bank's call center faced a problem due to the large volume of inquiries about loan policies. The human agents could not provide real-time explanations for complex terms, resulting in a customer satisfaction rate of less than 80%.
Solution:
After integrating the large model knowledge assistant, the agents can directly ask questions in the customer service system interface in voice form. The knowledge assistant provides explanations of the terms and the original policy text in real time, increasing the business processing efficiency by 60%.