Our projects
RAG Agent for Responding to Queries on Facebook Business page
Description: Designed and implemented a Retrieval-Augmented Generation (RAG) agent to manage and respond to user queries on a Facebook business page, focusing on customer inquiries about products and installment plans. The agent provides accurate and contextually relevant answers by leveraging a combination of retrieval-based and generative approaches.
Technologies Used: N8n, Python, PyTorch, Elasticsearch, Facebook Graph API.
Challenges: Integrating retrieval and generation models for seamless query handling and ensuring timely and contextually appropriate responses to diverse user queries.
Results: Enhanced user engagement and satisfaction by providing quick and accurate responses, leading to a 35% reduction in response time and a 20% increase in user interaction rates.






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