Case Study
Category:
Applied AI, RAG Systems, & Backend Engineering
Impact:
4 Months | Production-Grade Backend
Traditional task management platforms such as JIRA and ClickUp rely heavily on manual configuration and lack contextual understanding. They cannot reason over project goals, dependencies, or documentation. It was conceived to bridge this gap by introducing multi-agent intelligence capable of autonomously creating, managing, and prioritizing tasks based on contextual project data. The project required a robust backend architecture, intelligent RAG pipelines, and seamless integration with Knowledge Graphs to deliver human-like reasoning and adaptive automation.
Developed the complete backend from scratch using FastAPI, AWS, and PostgreSQL, ensuring modularity, speed, and fault tolerance. Integrated MCP (Model Control Protocol) to enable seamless communication across multiple connected servers.
Engineered high-accuracy Retrieval-Augmented Generation pipelines by combining Knowledge Graph reasoning with dense vector retrieval. This hybrid structure enabled YBA.ai to understand context, hierarchy, and task dependencies with exceptional precision.
Built a multi-agent architecture where specialized AI agents collaborated to parse project goals and documentation, generate structured subtasks, and prioritize and optimize workload dynamically.
Implemented an Airflow-based scheduler to manage RAG pipeline execution efficiently, ensuring reliability, scalability, and low latency across workflows.
