AI-Powered Financial Advisory Copilot Implementation with Voice-enabled 3D Avatars
To resolve complex financial queries in fun and engaging way, because fetching multiple documents to get answers is time-consuming and boring.
At a Glance
This case study outlines our collaboration with a leading FinTech firm to develop an advanced advisory financial copilot.
Designed for financial advisors and tax professionals, the platform integrates features such as voice interaction and a 3D avatar to deliver an intuitive and immersive user experience.
Key Highlights
LLM-based Financial Advisory Copilot
Voice-Enabled Interaction with a 3D Avatar
Retrieval-Augmented Generation (RAG)
Dynamic Facial Expressions and processing layers
NVIDIA Audio2Face for 3D Avatar Expression
User-Centric Design with Text & Voice Interactions
Challenges
- Consistent & high-quality responses under concurrent load
- Real-time accuracy in voice-to-text processing
- Cloud infrastructure migration & monitoring
- Data security and compliance with financial data sensitivity
- Reliable response generation without latency
- Synchronization of facial expressions and voice
About Client
The client is a leading FinTech firm focused on platforms for financial advisors and tax professionals. They aim to enhance advisory efficiency through advanced AI solutions that provide precise and timely information retrieval.
FinTech
Industry
Advisory
Domain
USA
Location
How we helped?
We developed the financial copilot by integrating advanced features. This involved implementing a Retrieval-Augmented Generation (RAG) approach to enhance query accuracy, integrating a voice-enabled 3D avatar for interactive responses, and ensuring data security through selective information processing.
2.26X
Increase in User Engagement through 3D Avatars
53%
Improved Response Accuracy with the RAG Approach
20 Sec
Reduced Response Times from 20 Minutes to 20 Seconds
The Solution
The solution is a robust AI-driven advisory financial copilot that combines advanced technologies to meet the specific needs of tax professionals. Here is a detailed breakdown of the solution:
Comprehensive AI Chatbot:
Text-Based Interaction
The platform features a text-based chatbot that uses an RAG based approach to access a pre-processed knowledge base, ensuring accurate responses to complex financial queries.
Integrating advanced AI technologies in financial advisory platforms sets a new standard for user interaction and efficiency.
Combining text and voice interactions with a dynamic 3D avatar, we have created a solution that not only addresses the specific needs of tax professionals but also demonstrates the transformative potential of AI in the financial services sector.
Voice-Enabled Interaction:
Users can interact with the chatbot using voice commands. The system includes a voice-to-text layer for converting spoken queries into text, processed by the chatbot to generate responses.
3D Avatar with Facial Expressions:
A dynamic 3D avatar responds to user queries with synchronized facial expressions and speech to ensure a seamless user experience with high-quality avatar animations and voice responses.
Advanced Backend Architecture
Retrieval-Augmented Generation (RAG):
➡️ The RAG approach integrates a knowledge base with a vectorized data model to improve the accuracy of responses.
➡️ This system processes complex queries by retrieving relevant information and generating precise answers.
Voice Processing Layers:
➡️ The voice interaction feature includes multiple layers: transcription for converting speech to text, an orchestrator for processing queries, and a text-to-audio layer for converting responses into speech.
➡️ Facial expression and animation layers enhance the avatar’s interactivity.
Integration with OpenAI:
➡️ The vectorized data from SageMaker is utilized by OpenAI to generate precise and relevant responses.
➡️ This integration ensures that the AI’s answers are informed by accurate and contextually appropriate data, thereby enhancing the overall reliability of the advisory platform.
Scalable Infrastructure Implementation
Cloud Infrastructures & Deployment:
The platform employs AWS services such as EC2 instances, S3, and load balancers to support its operations & it is being hosted on Azure VM with GPU.
AWS SageMaker Implementation:
➡️ AWS SageMaker was instrumental in building a comprehensive knowledge base by processing and vectorizing data.
➡️ This data, sourced from various financial documents, is converted into vectors stored in a database to enhance response accuracy and minimize the risk of AI-generated inaccuracies.
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