Case Study
Category:
Conversational AI, Financial Technology, & RAG Systems
Impact:
2 Months | Hybrid | Contract
Financial institutions often struggle with managing millions of daily customer interactions efficiently while maintaining context awareness and accuracy. Qi-Card, a leading Iraqi financial services provider, required an AI-driven chatbot capable of handling 2M+ daily requests, interpreting text and image inputs, and ensuring uninterrupted service reliability. A scalable, multimodal conversational system was needed—one that could integrate LLMs, RAG pipelines, and fallback intelligence to deliver a seamless customer experience.
Developed a robust fallback mechanism leveraging AWS Bedrock and LLMs to maintain smooth user interaction during API failures and edge cases. Optimized request routing for resilience and reduced latency under peak load.
Created an advanced image ingestion feature enabling the chatbot to understand screenshots, receipts, and document images. Integrated computer vision and OCR modules to extract contextual data for more accurate and personalized responses.
Architected a dedicated Retrieval-Augmented Generation (RAG) pipeline for contextual data retrieval and response enrichment. Employed Apache Airflow for task orchestration, monitoring, and optimization of RAG execution cycles.
Developed comprehensive unit and integration tests to ensure code reliability, minimize regression, and maintain performance across continuous deployments.
