Retrieval Augmented Generation (RAG) is transforming how businesses use Large Language Models (LLMs) by overcoming challenges like outdated data, domain knowledge gaps, and hallucinations. It does this by connecting LLMs to external, up-to-date knowledge sources
From surfacing the latest product specs in support chats to pulling real-time offers for sales teams and generating insights from internal knowledge bases, enterprises are adopting Retrieval Augmented Generation (RAG) to power AI systems that deliver up-to-date, context-aware outputs.
Why Traditional RAG Falls Short
Native RAG looks simple, but scaling it exposes hidden challenges. As systems grow, inefficiencies at every stage can lead to incomplete or inaccurate answers—putting trust and reliability at risk.
Category
Challenge
Impact
Data Ingestion & Preparation
Ineffective / Naive Chunking
Leads to irrelevant context, poor retrieval accuracy, and unreliable information.
Use of Generic Embedding Models
Results in poor understanding of domain-specific queries and retrieval of irrelevant documents.
Poor Data Quality & Organization
Impacts output consistency and introduces bias, especially when processing unstructured data
Information Retrieval
Poor Query Understanding & Semantic Ambiguity
Leads to irrelevant or imprecise results, failing to capture user intent and reducing overall system reliability.
Imbalance in Precision & Recall
Overlooks key retrieved content, resulting in incomplete or misleading responses.
"Lost in the Middle" Phenomenon
Overlooks key information retrieved by the system, resulting in incomplete answers
Context Augmentation
Context Window Limitations
Truncates essential information, weakening the LLM’s ability to respond accurately
Disjointed or Incoherent Context
Confuses the LLM and produces illogical or nonsensical responses.
Response Generation
Persistent Hallucinations & Factual Inaccuracies
Undermines output reliability and credibility
Generic or Irrelevant Responses
Fails to deliver user value or address specific needs.
The Nurix Advancement: Enterprise-Grade RAG
At Nurix AI, we know that effective enterprise RAG requires more than just plugging in a basic setup. Our advanced platform is purpose-built to solve real-world challenges and drive measurable value. It delivers the reliability and intelligence your AI agents need—at scale.
Here’s how Nurix AI’s advanced RAG capabilities make the difference:
Advanced Chunking Strategies
Employing Contextualized Semantic Chunking, Document-based, Agentic, and Hybrid Hierarchical Chunking to preserve meaning and optimize retrieval, minimizing irrelevant context.
Unified & Fine-Tuned Embeddings
Unified model generates task-specific embeddings (query, document, retrieval). We also fine-tune models for your domain, ensuring superior relevance and accuracy.
Hybrid Search Excellence
Combining semantic and symbolic (keyword) search maximizes relevant context retrieval and reduces missed information, outperforming single-method approaches.
Intelligent Data Transformation
Transformation that includes Table-to-Text conversion, making structured data easily understandable and retrievable for the RAG system, enhancing data utility.
Sophisticated Query Processing
Automated query rewriting and decomposition transform ambiguous or multi-intent user prompts into optimized sub-queries for precise retrieval.
Optimized Reranking & Recall
Advanced reranking algorithms surface the most contextually accurate results first. Strategies are optimized for high recall, capturing even weak signals.
Flexible LLM Integration
Supports dynamic orchestration across 20+ LLMs (OpenAI, Claude, Gemini, Llama 3, etc.), enabling adaptive model selection.
Scalable & Multilingual
Low-latency, high-throughput architecture built for enterprise scale. Natively processes and retrieves content across multiple languages.
Nurix AI moves beyond the "RAG 1.0" phase, bringing the AI engineering maturity and architectural foresight required to build robust, production-grade systems. Our solutions are designed for real-world scale, model-agnosticism, and seamless integration with your existing systems—like CRMs, telephony, and knowledge bases. Led by experts and trusted by leading brands, Nurix turns scattered enterprise knowledge into instant, contextual, and accurate answers
