Ethiopian Financial Data Hub

The primary obstacle was 'Unstructured Data Fragmentation.' In the Ethiopian financial landscape, standard APIs for exchange rates or bank performance are virtually non-existent. Data is often published as low-resolution images or diverse PDF formats daily. The AI system needed to automate the collection, extraction, and validation of this data with near-perfect accuracy to maintain trust. Additionally, the agents had to handle Amharic and English mixed content, varying document layouts, and inconsistent formatting across hundreds of sources.

We built a cutting-edge 'Agentic AI' pipeline with specialized LLM-based autonomous agents. Each agent has a defined role: one scrapes and monitors banking portals, another parses unstructured documents using vision-capable models for images and tables, and a third performs multi-step cross-validation against historical data to flag anomalies. The agents operate on a schedule with human-in-the-loop review for edge cases. We implemented a RAG-enhanced Knowledge Base for contextual retrieval and used fine-tuned extraction prompts for Ethiopian financial terminology. The AI assistant is powered by multilingual LLMs, supporting natural language queries and responses in Amharic, Afaan Oromo, and Tigrigna, enabling inclusive access to financial intelligence across Ethiopia's diverse linguistic landscape. The frontend was developed with Next.js 16+, utilizing Server Components for high-speed delivery of complex charting data, while a Django-based REST API manages the historical time-series database.