Automated Document Analysis and Data Extraction with Generative AI
- 70% Accuracy Improved
- 25% Optimized Cost
- 90% Reduced Manual Work
Business Requirements
Our client is a banking and financial services provider who aims to deliver scalable solutions. They focus on customer-centricity and continuously explore technologies to optimize operational workflows and enhance user experiences. They faced challenges with their existing manual document processing processes as those were time-intensive and prone to errors, resulting in inefficiencies and higher operational costs.
To overcome these challenges, the business wanted an automated document data extraction solution to accurately identify attributes, extract relevant data fields, and assign confidence scores to ensure validation. The ultimate goal was to modernize document processing workflows while maintaining high accuracy and scalability. Whether you're looking for trends, comparisons, or specific data points, IndexAI's Data Questions feature provides quick, accurate, and visually appealing answers. This intuitive approach to data analysis empowers teams across your organization to make data-driven decisions with ease.
Solution
Real-Time Voice Interaction
- Implemented Twilio for seamless speech-to-text (STT) and text-to-speech (TTS) conversion.
- Established an automated outbound calling system to initiate reminders and handle inbound queries.
- Leveraged WebSocket for real-time audio streaming and communication.
Context Management
- Integrated Redis stores and retrieves conversation history, ensuring continuity in multi-turn interactions.
- Utilized vector databases (Weaviate) to match user queries with prior conversations for enhanced context awareness.
Natural Language Processing (NLP)
- Integrated OpenAI’s LLM to process voice-to-text inputs and generate human-like responses.
- Fine-tuned AI models for domain-specific conversations, ensuring relevant and accurate responses to associates.
Query Processing and Response Generation
- Combined vector search with LLM to dynamically generate answers based on prior interactions.
- Implemented caching mechanisms for frequently asked questions to reduce response time.
- Used response templates to ensure consistency and clarity across different scenarios.
Query Processing and Response Generation
- Combined vector search with LLM to dynamically generate answers based on prior interactions.
- Implemented caching mechanisms for frequently asked questions to reduce response time.
- Used response templates to ensure consistency and clarity across different scenarios.
