RAG DEVELOPMENT · INDIA

RAG Development Company — AI That Knows Your Data

We build Retrieval Augmented Generation (RAG) systems that let your AI answer from your own documents, databases, and knowledge base — accurately, in real time, with source citations.

Build Your RAG System View RAG Case Studies

What is RAG?

Why RAG Beats Standard ChatGPT

Standard LLMs hallucinate and have a knowledge cutoff. RAG solves both problems by grounding AI answers in your real, up-to-date data.

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Document RAG

Upload PDFs, Word docs, manuals, SOPs, and contracts. Your AI answers questions from them instantly, with page references.

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Database RAG

Connect your CRM, ERP, or SQL database. AI queries your live data and responds with accurate, real-time information.

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Website RAG

Crawl and index your entire website. Build a chatbot that knows every product, FAQ, policy, and blog post you've published.

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Multi-Source RAG

Combine documents, databases, APIs, and web sources into one unified knowledge base your AI can search across simultaneously.

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Hybrid RAG

Combine vector search + keyword search + reranking for maximum retrieval accuracy, even on complex or ambiguous queries.

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Agentic RAG

RAG + AI agents that can plan, reason, search multiple sources, and take actions — not just answer questions but complete tasks.

Technology Stack

RAG Tech We Use

We use industry-leading tools and frameworks to build production-ready RAG systems.

LangChain LlamaIndex OpenAI GPT-4o Claude 3.5 Sonnet Pinecone Weaviate Chroma FAISS Qdrant FastAPI Python PostgreSQL + pgvector AWS / Azure / GCP Docker + Kubernetes

RAG Use Cases

What RAG Can Do For You

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Customer Support Bot

Answer customer queries from your product docs, FAQs, and past support tickets. Reduce tickets by 60-80%.

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Healthcare Knowledge Base

Doctors and staff query clinical protocols, drug interactions, and patient records through natural language.

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Legal Document AI

Search and summarize contracts, case files, and regulations. Reduce legal research time by 70%.

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Financial Report AI

Query annual reports, balance sheets, and market data. AI answers investment questions from structured data.

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Internal Knowledge Bot

Employee onboarding, HR policy queries, IT helpdesk — answered instantly from your internal documentation.

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E-Commerce Product AI

Answer product comparison questions, check inventory, and recommend products from your catalog database.

FAQ

RAG Questions Answered

What is RAG (Retrieval Augmented Generation)? +

RAG is an AI architecture that combines a large language model (LLM) with a retrieval system. When a user asks a question, RAG first retrieves relevant chunks from your documents, then feeds them to the LLM to generate an accurate, grounded answer — reducing hallucinations significantly.

What documents can RAG work with? +

RAG can work with PDFs, Word docs, Excel sheets, websites, databases, APIs, emails, Notion pages, Confluence wikis, SharePoint, and any structured or unstructured data source.

How accurate is RAG vs a standard chatbot? +

RAG-based chatbots are significantly more accurate because answers are grounded in your actual data. They cite sources, avoid hallucinations, and stay up-to-date as your documents change.

How long does it take to build a RAG system? +

A basic RAG chatbot for a single document set can be ready in 2-3 weeks. Complex multi-source, multi-tenant RAG systems with custom UI take 6-10 weeks. We provide a free consultation and timeline estimate upfront.

Can RAG work with my existing systems? +

Yes. We build RAG integrations for Salesforce, HubSpot, Notion, Confluence, SharePoint, Google Drive, custom databases, REST APIs, and most enterprise systems.