I build AI systems that search your documents, understand context, and answer questions accurately -- grounded in your actual company data, not AI hallucinations.
RAG (Retrieval-Augmented Generation) is the architecture behind every AI system that needs to answer questions from specific data. Instead of relying on a language model's general knowledge, RAG first searches your documents to find relevant information, then uses that context to generate precise, factual answers with source citations.
This is how companies like Notion, Slack, and Confluence are adding AI search to their products. The difference: I build custom RAG systems tailored to your specific data, business logic, and security requirements. Your data stays on your infrastructure, and the system is optimized for your exact use case -- not a one-size-fits-all solution.
I am Kirill Strelnikov, a freelance AI integration developer based in Barcelona. I have built RAG systems for customer support knowledge bases, internal documentation search, and product catalogs. My RAG applications achieve 90-95% factual accuracy with source citations on every answer.
RAG is the right solution whenever your AI needs to work with specific, up-to-date information rather than general knowledge. Here are the three most common applications.
AI-powered search across your company wiki, SOPs, HR policies, and technical documentation. Employees ask questions in natural language and get instant, accurate answers with links to the source document.
A chatbot trained on your product docs, FAQs, and support history. It answers customer questions accurately, cites specific documentation, and escalates to human agents only when needed. Reduces support tickets by 60-70%.
AI that analyzes contracts, research papers, legal documents, or regulatory filings. It can compare documents, extract key clauses, summarize findings, and answer complex multi-document questions with evidence.
RAG works with any text-based data: PDFs, Word docs, Confluence pages, Notion databases, Slack messages, emails, code repos, and API documentation. For conversational AI needs, see my AI chatbot development service.
Building a production RAG system requires careful selection of embedding models, vector databases, chunking strategies, and retrieval methods. Here is the stack I use.
Embedding & retrieval: I use OpenAI Ada-3 or Cohere embeddings for semantic search, combined with hybrid retrieval (vector + keyword search) for maximum accuracy. Chunking strategy is tailored to your document types -- technical docs need different chunking than legal contracts.
Vector storage: pgvector for PostgreSQL-native solutions (no extra infrastructure), Pinecone for high-scale production, or ChromaDB for rapid prototyping. The choice depends on your scale, budget, and existing infrastructure.
Generation layer: GPT-4o or Claude for answer generation with custom prompts that enforce citation, format control, and domain-specific reasoning. Every answer includes source references so users can verify the information.
Understanding when RAG is the right approach saves time and money. Here is how it compares to alternatives:
Bottom line: If your AI needs to answer questions from specific, changing data, RAG is the right approach. If you need to change how the AI writes or reasons, fine-tuning might help. Most business use cases need RAG.
I analyze your document corpus: formats, volume, update frequency, and quality. I identify the best chunking strategy, embedding model, and retrieval approach for your specific data. Deliverable: technical specification with architecture diagram and cost estimate.
I build custom parsers for each document type (PDF, Word, HTML, Confluence, etc.). Documents are chunked, cleaned, and embedded into your vector database. Metadata extraction ensures accurate filtering and source attribution.
I tune the retrieval pipeline for your data: hybrid search (semantic + keyword), re-ranking, query expansion, and context window optimization. This phase turns a basic RAG into a production system with 90%+ accuracy.
I build the user interface -- web app, API endpoint, Slack bot, or widget -- and integrate it with your existing systems. Every answer includes source citations and confidence indicators. Role-based access controls who can see what.
Systematic testing against a question bank covering your key use cases. I measure retrieval accuracy, answer quality, and latency. Continuous monitoring tracks system performance after launch, with automatic alerts for quality degradation.
Built a RAG-powered chatbot for an online clothing store that answers questions from the product catalog, size guides, shipping policies, and return procedures. The system automated 70% of customer support queries and increased conversion by 35% through personalized product recommendations grounded in actual catalog data.
Created a Telegram bot that unifies multiple AI models with document processing capabilities. Users upload documents and ask questions, receiving answers grounded in the uploaded content. The system processes PDFs, images, and text files with automatic language detection and multilingual responses.
Fixed-price contracts based on complexity. All prices include document pipeline, retrieval optimization, application development, testing, and 30 days of post-launch support.
Monthly running costs: EUR 50-300 depending on document volume and query frequency. Includes LLM API fees, vector database hosting, and compute.
Kirill Strelnikov is a freelance AI engineer based in Barcelona, Spain. He specializes in RAG systems, AI agent development, and AI integration for European businesses. His RAG implementations have automated customer support, powered internal knowledge search, and processed thousands of documents for accurate AI-driven Q&A.
Core stack: Python, LangChain, LlamaIndex, OpenAI, Claude, pgvector, Pinecone, Django, PostgreSQL. Fixed-price contracts with clear deliverables. Communication in English, Spanish, and Russian.
Tell Kirill about your data and use case. He will propose a RAG architecture, estimate the timeline, and give you a fixed price -- within 24 hours. Free consultation, no commitment.
Book a free RAG consultation