Product/Service Description
Addressing the challenge of dispersed and hard-to-access internal knowledge, this solution aims to create robust, enterprise-specific knowledge bases. The core technology is Retrieval Augmented Generation (RAG), which synergizes the generative power of Large Language Models (LLMs) with precise information retrieval, significantly mitigating the risk of AI generating inaccuracies or "hallucinations."
Aortac¡¦s RAG implementation includes meticulous data preparation (semantic chunking of documents, vectorization, metadata addition), sophisticated retrieval and ranking mechanisms (query understanding, adaptive retrieval strategies, re-ranking for relevance), and rigorous generation and validation steps (agentic self-correction, source citation for traceability, multi-dimensional correctness checks).
Diverse RAG architectures are available to suit various business scenarios: "Multi-turn Conversational RAG" for contextual understanding in applications like new employee training; "Self-RAG" with self-verification capabilities for high-stakes domains like medical or legal compliance; "Agentic RAG" for multi-tool reasoning and complex task decomposition in cross-system integrations; and "Graph-RAG" for dynamic knowledge base routing in multi-domain complex queries. Vectorization of knowledge snippets and semantic search are key to efficient and relevant information retrieval within the RAG framework.
Aortac¡¦s RAG implementation includes meticulous data preparation (semantic chunking of documents, vectorization, metadata addition), sophisticated retrieval and ranking mechanisms (query understanding, adaptive retrieval strategies, re-ranking for relevance), and rigorous generation and validation steps (agentic self-correction, source citation for traceability, multi-dimensional correctness checks).
Diverse RAG architectures are available to suit various business scenarios: "Multi-turn Conversational RAG" for contextual understanding in applications like new employee training; "Self-RAG" with self-verification capabilities for high-stakes domains like medical or legal compliance; "Agentic RAG" for multi-tool reasoning and complex task decomposition in cross-system integrations; and "Graph-RAG" for dynamic knowledge base routing in multi-domain complex queries. Vectorization of knowledge snippets and semantic search are key to efficient and relevant information retrieval within the RAG framework.
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