Industry: Legal / Financial Services (Debt Settlement)
Cloud: AWS (Serverless + GenAI + Vector Search)
Challenge:
The client faced operational bottlenecks due to high volumes of incoming legal correspondence. Manual identification, classification, and Salesforce updates created delays, data errors, and inefficiencies—especially for non-standard document formats.
Nuage Solution:
Nuage designed and deployed a cloud-native Intelligent Document Processing platform leveraging AWS serverless services, Retrieval-Augmented Generation (RAG), and vector-based semantic search to automate classification, data extraction, and system synchronization.
How Nuage Delivered
- AI-Driven OCR & Content Extraction – Documents were converted into optimized image batches and processed via AWS Bedrock (Claude) to extract structured and unstructured content while minimizing token usage.
- Semantic Classification – Extracted content was transformed into vector representation using Amazon Titan v2 and stored in Qdrant Vector DB. Similarity search was applied to group and classify documents for intelligent classification.
- Confidence-Based Automation – High-confidence matches (>0.9) were auto-classified, while lower-confidence cases were routed to human review—creating a continuous learning feedback loop.
- AI-Powered Address Intelligence – Claude parsed messy creditor address formats into clean, validated JSON outputs, eliminating downstream data errors.
- Real-Time Salesforce Integration – Bi-directional synchronization ensured instant updates without duplicate entry.
- Performance Acceleration – High volume functions were developed in Rust to minimize latency and optimize throughput.
Business Impact
- 80% reduction in manual document processing workload
- Improved classification accuracy and data integrity
- Real-time Salesforce synchronization
- Scalable serverless architecture supporting growth