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Retrieval-augmented generation
RAG Development (Retrieval-Augmented Generation)
RAG development connects language models with your company knowledge: for every question, the system finds the relevant documents and formulates an answer with source references. Context Studios, an AI-native development studio in Berlin, builds such systems with vector databases, hybrid search and quality measurement – GDPR-compliant and connected to your existing systems.
Context Studios — RAG systems from BerlinPinecone · Weaviate · Convex · LlamaIndexGDPR-compliant · EU AI ActBerlin-Charlottenburg
- Workshop
- Setup
- Sprint
- Build & Support
Fixed price after scoping · proposal within 48 h
Last updated:
(01)
What is RAG development?
AI technology
Retrieval-augmented generation (RAG) is an AI architecture in which a language model retrieves relevant passages from your own documents before every answer. The model answers on the basis of these sources and cites them. This makes answers more current, verifiable and far less prone to hallucinations – without retraining the model.
- Specialisation
- Vector databases, embedding strategies, hybrid search, re-ranking
- Technologies
- Pinecone, Weaviate, pgvector, Convex, Cohere Rerank
- Target group
- Companies with extensive knowledge bases and document collections
- Project duration
- Typically 4–12 weeks for production-ready RAG systems
- Compliance
- GDPR, data localisation, access control, audit trails
LLM integrationVector database integrationAI document processingChatbot developmentLLM fine-tuning
(02)
What does a RAG system include?
From document preparation to a production-ready knowledge base
(01)
Intelligent document preparation
Optimised embedding pipelines: documents, knowledge bases and FAQ collections are prepared and embedded precisely — with embedding models that suit your data.
(02)
Vector database architecture
Selection and setup of the right vector database — such as Pinecone, Weaviate, pgvector or Convex — with indexes that respond quickly even with large data volumes.
(03)
Semantic search & hybrid retrieval
Hybrid retrieval: a combination of semantic vector search and classic keyword matching to maximise recall and precision at the same time.
(04)
Chunking strategy & embedding optimisation
Intelligent document splitting for optimal context windows — with the right balance between granularity and context.
(05)
Answer evaluation & quality assurance
Automatic evaluation of answer quality, relevance and completeness. Every answer references the underlying documents and remains traceable.
(06)
Incremental data updates
Automatic indexing: new and changed documents are continuously embedded and made searchable — your knowledge base stays up to date.
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How is a RAG system built?
- (01)
Consultation
Free 30-minute initial call via video. We get to know your knowledge sources and typical questions, identify suitable use cases and give you a first assessment of feasibility and timeline.
Day 1 - (02)
Proposal & planning
You receive a written proposal with scope, timeline and fixed price – including data sources, a permissions concept and an evaluation plan.
Days 2–3 - (03)
AI-accelerated development
Agile development with weekly demos. Goal: a working MVP in about 4 weeks, with production-ready code and automated tests.
Weeks 1–4 - (04)
Launch & support
Production deployment with complete documentation and 30 days of free bug fixing from final delivery. Maintenance and further development by agreement.
Week 4+
Frequently asked questions about RAG systems
(01)What is the difference between RAG and fine-tuning?
RAG combines search with text generation: first the relevant documents are found, then the language model formulates an answer based on these sources. Fine-tuning, by contrast, adapts the model itself, for example to a style or technical vocabulary. RAG is particularly suitable when knowledge changes frequently and answers must be verifiable; both approaches can also be combined.
(02)Which document formats does a RAG system support?
A RAG system processes all common formats: PDF, Word, HTML, Markdown, emails, presentations and structured data from databases or spreadsheets. Content from Confluence, SharePoint, Google Drive or ticketing systems can also be connected via interfaces. We make scanned documents searchable with text recognition and prepare tables and charts separately so that no information is lost.
(03)How many documents can a RAG system process?
Very large collections too: modern vector databases search large numbers of text passages quickly, and optimised indexes keep response times stable as the collection grows. What matters most for quality is not volume but preparation – sensible passages, good metadata and up-to-date content. That is why we often start with the most important sources and expand step by step.
(04)How current is the data in a RAG system?
New and changed documents are indexed automatically and are then available for answers; deleted documents disappear from the index. We agree with you how often updates run – continuously via webhook, hourly or nightly. With every answer, users see the date of the source, so outdated information stands out immediately.
(05)How do RAG systems prevent hallucinations?
Through source grounding: the system answers only on the basis of the documents it finds and cites the sources. If it finds no suitable passage, it says so openly instead of guessing. In addition, we automatically check answers for consistency with the sources. This reduces hallucinations significantly and makes every answer verifiable for your employees.
(06)What does a RAG system cost to run?
Running costs depend on data volume, request volume, the chosen model and hosting; API costs depend on the model and volume. With caching and suitable embedding models we keep them predictable. For development: fixed price after scoping, proposal within 48 hours. A good way to start is a fixed-price workshop in which we prioritise data sources and use cases.
(07)Can existing search systems be replaced by RAG?
Often yes, or RAG complements them. Classic keyword search returns lists of hits, while a RAG system answers the question directly and points to the relevant passages. We often combine both in a hybrid search. Via APIs we connect the system to your CRM, helpdesk, intranet or chat tools, so your teams can use it where they already work.
(08)Which vector database do you recommend?
That depends on data volume, hosting requirements and budget. We use Pinecone, Weaviate, pgvector and Convex, among others. pgvector is a good fit if you already use PostgreSQL; specialised services pay off for very large collections or complex filters. We recommend the right solution after scoping, with reasons based on your requirements.
(09)How do we measure the quality of a RAG system?
Through automatic evaluation: we measure the relevance of the retrieved sources as well as the precision and completeness of the answers with test questions from your daily work, for example with tools such as ragas. We also collect user feedback in live operation. This makes quality measurable, and changes to chunking, search or prompts can be assessed in a targeted way.
(10)Does our data stay under our control with RAG?
Yes. We rely on GDPR-compliant hosting in the EU, access controls and encrypted storage. The system respects existing permissions, so employees only receive answers from documents they are allowed to see anyway. On request, we run the vector database and language model entirely in your own infrastructure, without external providers.
Related pages
- AI integrationOverview: connecting AI systems with your IT
- Vector database integrationBuilding and integrating vector databases
- LLM integrationConnecting language models securely to your systems
- AI data pipeline developmentData preparation and ETL pipelines for AI
- LLM fine-tuningDomain-specific model adaptation
(04)
Our RAG technology stack
(01)
AI & ML
Anthropic ClaudeOpenAI GPTGoogle GeminiOpen-Source LLMs (Llama, Qwen, DeepSeek, Mistral)ConvexRAG & Vector DBs (Pinecone, Weaviate)MCP (Model Context Protocol)Hugging Face TransformersComputer Vision (YOLO, SAM)ElevenLabs (Voice AI)Google Veo (Video AI)
(02)
Web & Mobile
Next.js 16 & React 19TypeScriptReact Native & ExpoTailwind CSS v4Shadcn/uiVercel Edge Runtime
(03)
Backend & Data
Node.js & Hono (Edge)PythonPostgreSQL & SupabaseConvex (Real-Time DB)RedistRPC & GraphQLOpenAPI 3.1
(04)
DevOps & Infrastructure
Vercel & AWSDocker & KubernetesCI/CD-Pipelines (GitHub Actions)OpenTelemetry & GrafanaLangfuse (LLM Monitoring)
(05)
RAG solutions by industry
Pharma & life sciences
Search medical literature, studies and guidelines intelligently — as support for research and professional decisions.
Technical documentation
Make manuals, specifications and knowledge bases searchable — for precise, source-based answers in support and service.
Insurance
Research in policy terms, reports and regulatory documents — reaching verifiable answers faster.
Consulting & professional services
Search project knowledge, policies, manuals and training materials intelligently — answer employee questions immediately.
Public sector
Semantic search in regulations, administrative documents and publications — for faster, traceable information.
(06)
Example projects
Examples we can build for you
Customer service
AI-powered support agent
An AI-powered support agent that delivers source-based answers from the knowledge base. Goal: handle most requests automatically — with precise, verifiable answers.
Automated first response · Multilingual · Available 24/7
Knowledge management
RAG-based document system
A document system for thousands of specialist documents: semantic search with source references that can speed up research considerably.
Source-based answers · Fast search · Scalable
Process automation
Workflow automation with AI agents
RAG for workflow automation: AI agents access company knowledge in context and can thus reduce manual research work.
End-to-end automated · Fewer errors · Time savings
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RAG development — consultation in Berlin
- Founder AI-native since
- 2024
- Phone
- +49 30 96610664
- info [at] contextstudios [dot] ai
RAG systems for your company
Make your company knowledge intelligently searchable. Discuss your RAG project in a 30-minute call directly with the founder.
- First call: 30 minutes by video
- Directly with the founder, Michael Kerkhoff
- Free of charge, no obligation
Context Studios · Kaiser-Friedrich-Str. 6 · 10585 Berlin