AI Sales Agent Training with Company Data and Guidelines
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Solution Overview
Problem
Existing automated user interactions in computer networks are lengthy, complicated, and fail to connect with diverse customer needs and preferences, leading to customer frustration and drop-off.
Innovation Solution
A computer-implemented method utilizing a large language model trained with company data and sales guidelines to derive a trained model for automated user interactions, incorporating a stage analysis agent and conversation generation agent to personalize and streamline customer onboarding and sales processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional automated user interactions are used, then automation is achieved, but the process becomes lengthy and complicated leading to customer frustration
Solution Approach 1:
The patent changes the operational parameters of the automated interaction system by implementing dynamic stage analysis and real-time conversation adaptation. The system transitions from static, pre-scripted automation to dynamic parameter-adjusted interactions that respond to customer cues, thereby maintaining automation while reducing perceived complexity and frustration
2Adaptability or versatility
If traditional sales tools are used, then generic experience is provided, but diverse customer needs and preferences are not connected
Solution Approach 1:
The system performs preliminary actions by pre-defining multiple conversation stages and customer personas before actual interactions occur. This allows the system to quickly adapt to diverse customer needs without building complex personalization logic in real-time, as the framework is prepared in advance
Solution Approach 2:
The patent implements dynamic conversation flow that adapts in real-time based on customer responses and stage analysis. The system transitions from static sales scripts to dynamic interactions that adjust tone, content, and pacing based on detected customer needs, achieving versatility without excessive complexity
3Loss of information
If lengthy onboarding processes are used, then comprehensive information is collected, but customer frustration and drop-off increase
Solution Approach 1:
The patent segments the onboarding process into distinct stages (introduction, needs analysis, solution presentation, closing) with clear objectives for each. This segmentation allows the system to collect comprehensive information progressively rather than presenting a lengthy monolithic process, reducing customer frustration while maintaining information completeness
Solution Approach 2:
The system implements continuous feedback loops where customer responses are analyzed in real-time to adjust the conversation flow. This allows the system to collect necessary information efficiently by adapting to customer engagement levels, pausing or revising stages when frustration is detected, thereby reducing drop-off while maintaining information gathering
Data Source
AI summary
A computer-implemented method includes supplying a large language model with company data. The large language model is supplied with sales guidelines. A trained model is derived from the large language model. The trained model is utilized for automated user interactions.