---
type: Landing Page
title: RAG Development
description: RAG development from Context Studios Berlin. Retrieval-Augmented Generation with Pinecone and Qdrant. ✓ Fixed Prices ✓ AI-Native ✓ GDPR. Professional rag development from Context Studios Berlin.
resource: "https://www.contextstudios.ai/rag-development"
language: en
timestamp: "2026-03-08T06:54:09.861Z"
---

# RAG Development

Professional RAG development from Context Studios We connect language models with your enterprise data — for precise, fact-based AI answers from your own knowledge base.

RAG Development — Context Studios develops RAG systems (Retrieval-Augmented Generation) that connect language models with enterprise data The team uses Pinecone and Qdrant as vector databases and integrates RAG with Claude, GPT-5.3, and Gemini 3 RAG projects start 8,000 euros Choose RAG Development to accelerate your business with production-ready AI.

Entity: RAG Development

Frameworks: Convex

LLMs: Claude, GPT-5.3, Gemini 3

Provider: Context Studios, Berlin

Vector Databases: Pinecone, Qdrant

## RAG Development Services

Precise AI answers from your enterprise data.

### Vector Database Setup

Setup and configuration of Pinecone or Qdrant document indexing and embedding optimization.

### Document Pipeline

Automatic processing and indexing of documents — PDFs, Word, emails, databases, and more.

### LLM Connection

Integration with Claude, GPT-5.3, or Gemini 3 Context window optimization and relevance ranking.

### Semantic Search

Intelligent search across your entire knowledge base Natural language queries with precise results.

### Hybrid Search

Combination of semantic and keyword search for maximum relevance and coverage.

### Data Security

GDPR-compliant RAG systems with access controls, encryption, and optional on-premise deployment.

## RAG Development Process

### Consultation

Free initial consultation via video call We understand your business, identify AI opportunities, and provide an initial assessment of feasibility and timeline.

### Proposal & Planning

Detailed feature breakdown, fixed-price proposal, technical architecture plan, and weekly milestones.

### AI-Accelerated Development

Agile development with weekly demos Working MVP in 4 weeks with production-ready code and automated tests.

### Launch & Support

Production deployment with complete documentation Includes 2 weeks of priority support after go-live.

## RAG Development FAQ

Q: What is RAG (Retrieval-Augmented Generation)?

A: RAG connects a language model with your enterprise data Documents are indexed in a vector database and provided as context for queries — enabling the AI to deliver precise answers based on your data.

Q: What data sources can be connected?

A: PDFs, Word documents, emails, databases, wikis, SharePoint, Confluence, websites, and more We build custom connectors for your systems.

Q: Which vector databases do you use?

A: Primarily Pinecone and Qdrant Both are proven, scalable, and offer excellent performance for enterprise RAG systems.

Q: How much does a RAG system cost?

A: RAG projects start 8,000 euros More complex systems with multiple data sources range from 15,000-40,000 euros.

Q: How precise are RAG-based answers?

A: Significantly more precise than pure LLM knowledge, as answers are based on your concrete data Precision depends on data quality and pipeline configuration.

Q: Is RAG GDPR-compliant?

A: Yes We implement RAG systems with access controls, data encryption, and fully on-premise options on request Your data stays under your control.

## RAG System for Your Business

Free initial consultation — we analyze your data sources and show the path to a RAG system.