Indian genAI startup, Rabbitt.ai has announced the launch of ChanceRAG, a no-code Retrieval Augmented Generation (RAG) solution designed to simplify the integration of large language models (LLMs) with document retrieval systems.
Harneet Singh, chief AI officer at Rabbitt.ai, highlighted the product as an “enterprise-grade solution for building RAG.”
“We noticed that traditional retrieval methods, whether semantic or keyword-based, weren’t providing the depth and accuracy needed for complex queries. With ChanceRAG, we’ve created a fusion retrieval technique that delivers unparalleled precision and context, something that no current method achieves on its own,” he said.
ChanceRAG allows users to upload PDF documents and connect their LLMs to these documents through a vector database. The product introduces an Advanced Fusion Retrieval technique, which blends semantic understanding with keyword matching for enhanced performance.
Singh explained that the motivation behind ChanceRAG stemmed from the challenges businesses face in building effective RAG pipelines. He noted that existing retrieval methods were inefficient …