AI Development

RAG Systems That Know Your Business

We build Retrieval-Augmented Generation systems that give your AI accurate, up-to-date answers grounded in your own documents, databases, and knowledge bases — eliminating hallucinations.

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50+

RAG Systems Built

99%

Answer Accuracy

10x

Knowledge Retrieval

RAG Services

Our RAG Development Capabilities

Every component of a production-grade RAG system.

Document Ingestion Pipeline

Automated pipelines that ingest, chunk, and embed your documents — PDFs, Word files, web pages, and databases.

Vector Search

Semantic search using Pinecone, Weaviate, Chroma, and pgvector to retrieve the most relevant context for every query.

LLM Integration

Integration with GPT-4, Claude, Gemini, and open-source LLMs to generate accurate, grounded responses.

Hybrid Search

Combine semantic and keyword search for maximum retrieval accuracy across diverse document types.

Source Citation

Responses with cited sources — showing users exactly which documents the answer came from for trust and verification.

RAG Evaluation

Systematic evaluation of retrieval quality and answer accuracy using RAGAS and custom evaluation frameworks.

RAG Process

How We Build RAG Systems

A rigorous RAG development process that delivers accurate, reliable AI.

Knowledge Base Assessment

We assess your documents, data sources, and knowledge structure to design the optimal RAG architecture.

Ingestion & Embedding

We build the ingestion pipeline, select the best chunking strategy, and choose the optimal embedding model.

Retrieval & Generation

We implement the retrieval system, integrate the LLM, and tune the pipeline for accuracy and latency.

Evaluate & Optimise

We evaluate RAG performance systematically and optimise retrieval, prompts, and generation for production quality.

FAQ

Frequently Asked Questions

What is RAG and why do I need it?

RAG (Retrieval-Augmented Generation) grounds LLM responses in your specific documents and data, dramatically reducing hallucinations and ensuring accurate, relevant answers.

What types of documents can RAG handle?

RAG can handle PDFs, Word documents, PowerPoints, web pages, databases, Confluence pages, Notion docs, and virtually any text-based content.

How do you ensure the AI doesn't make up answers?

We implement strict retrieval thresholds, source citation, confidence scoring, and fallback responses for queries where relevant context isn't found.

Can RAG work with our internal knowledge base?

Yes. We connect RAG systems to Confluence, Notion, SharePoint, Google Drive, and custom knowledge bases via APIs and connectors.

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Why Arnnima Solution

Why Businesses Choose Us for RAG Development

We combine deep technical expertise, agile delivery, and a genuine commitment to your success — making us the partner of choice for RAG Development across India and globally.

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Retrieval-Augmented Generation systems grounding LLM responses in your authoritative knowledge base
Hybrid search combining dense vector retrieval with keyword search for maximum recall accuracy
Advanced chunking strategies and embedding optimisation for precise, contextual retrieval
Hallucination reduction through strict context grounding and output validation pipelines
Multi-document and multi-modal RAG handling PDFs, tables, images, and structured data
Private, on-premise RAG deployment ensuring sensitive enterprise data never leaves your environment

Technologies

Our Technology Stack

We use industry-leading tools and frameworks to deliver robust, scalable RAG Development solutions.

LLMs

GPT-4o Claude 3.5 Gemini 1.5 Llama 3.1

Vector DBs

Pinecone Weaviate Qdrant Chroma

Frameworks

LangChain LlamaIndex Haystack LangGraph

Industries

Industries We Serve with RAG Development

Our RAG Development solutions are trusted by businesses across diverse sectors.

Legal & Professional Services

Healthcare & Pharma

Financial Services

Enterprise Knowledge Management

Education & Training

Government & Public Sector

Client Stories

What Our Clients Say About Our RAG Development

Real results from real businesses who trusted Arnnima Solution with their RAG Development needs.

"The RAG system Arnnima built over our 50,000-document legal library answers complex research queries with 96% accuracy. Our lawyers save 4 hours per research task."

Jonathan PierceManaging Partner, LexFirst LLP

"Hybrid search architecture combining BM25 and vector retrieval gives our internal knowledge bot recall rates that semantic-only RAG couldn't match. Brilliant engineering."

Sneha KumarHead of AI, PharmaCorp India

"Our customer support RAG system processes queries against 3,000 product manuals and answers with cited source accuracy. Ticket deflection rate is 73%."

Mark SullivanVP Customer Success, TechManufacturer UK