What is RAG and how does it work?

RAG (Retrieval-Augmented Generation) is an AI architecture where a language model retrieves relevant information from your documents or database before generating an answer. This grounds responses in your actual data instead of relying on the model's training data, preventing hallucinations.

What types of documents can a RAG system process?

RAG systems can process PDFs, Word documents, Excel files, PowerPoint presentations, HTML pages, plain text, scanned documents (via OCR), database records, emails and structured data from APIs — in any combination.

How accurate are RAG applications?

With proper chunking strategy, embedding model selection, hybrid retrieval and reranking, RAG applications typically achieve 85–95% answer accuracy on well-structured document sets. We tune each component and provide source citations for every answer.

Can RAG be deployed on-premise for data privacy?

Yes. We deploy RAG applications fully on-premise or in your private cloud using open-source LLMs and self-hosted vector databases, so your documents never leave your infrastructure.

What vector databases do you use for RAG?

We select from Pinecone, Weaviate, ChromaDB, pgvector, Qdrant and Azure AI Search based on your data volume, query latency requirements, existing infrastructure and budget.

RAG Application Development

Your documents. Instantly searchable. Always accurate.

Retrieval-Augmented Generation connects your AI directly to your own documents, policies and databases — so every answer is grounded in your real data, with source citations, not hallucinated facts.

Build your RAG application All services

No hallucinations Source citations on every answer Data never leaves your environment

rag.knowledge-base.live

Ingestion flow Retrieval flow Your question

What we build

RAG applications across every document type and data source.

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Knowledge Base Search

Internal Q&A systems over your policy documents, SOPs, manuals, wikis and knowledge articles — accessible through chat or API.

📋

Contract & Legal RAG

Search and query contracts, agreements, compliance documents and regulatory filings to find clauses, obligations and risks instantly.

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Healthcare Knowledge AI

Clinical knowledge bases, patient documentation search and medical record intelligence with strict data privacy controls.

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Financial Document Intelligence

Query annual reports, financial statements, research notes and regulatory filings with audit-grade accuracy.

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Learning & Training AI

Employee training assistants and learning management search powered by your course content, guides and certification materials.

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Product & Technical RAG

Product documentation, technical specifications, API docs and engineering knowledge, searchable via natural language.

Technology

Built on the best RAG infrastructure.

We choose the right vector database, embedding model and LLM for your data volume, latency requirements and privacy constraints — whether cloud, hybrid or fully on-premise.

Discuss your requirements

aws Amazon Bedrock C Anthropic Claude Az Azure AI Search G Vertex AI Search Oa OpenAI Embeddings Lc LangChain Li LlamaIndex Ve Pinecone / Weaviate pg pgvector Ch ChromaDB

Process

How we build RAG applications.

Data & Document Audit

We assess your documents, databases and data sources — formats, volumes, quality, access controls — and define the ingestion strategy.

Chunking & Embedding Pipeline

We design and build the document processing pipeline — chunking strategy, embedding model selection, metadata extraction and vector storage.

Retrieval & Ranking

We implement and tune retrieval — hybrid search, reranking and context compression — to maximize answer accuracy and minimize hallucinations.

LLM Integration & Guardrails

We connect the retrieval layer to the LLM, implement system prompts, citation generation and content guardrails for brand-safe, accurate outputs.

UI, API & Deployment

We build the chat interface or API, integrate with your existing tools, and deploy to your cloud environment with monitoring and continuous improvement.

Related Services

Other ways we help you build with AI.

🤖

AI Agent Development

Autonomous agents that complete tasks, call tools and run workflows.

Learn more →

💬

AI Chatbots

Support, sales and HR bots with analytics, handoff and multilingual support.

Learn more →

🔗

Enterprise AI Integration

Connect AI into your CRM, ERP, databases and existing systems without rip-and-replace.

Learn more →

Start your AI project

Have an AI idea or business workflow in mind?

Talk to us before you build. We will help you turn your idea into a practical, AI-enabled roadmap with clear features, cost direction and implementation steps.

projects@nextwebi.com +91 9008372466 Mahadevpura, Bangalore 560048

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