RAG pipelines that actually answer
Retrieval-Augmented Generation grounds LLMs in your proprietary data — docs, wikis, CRM, knowledge bases. UseAIEasily ships production RAG systems from Budapest for Hungarian and international clients.
When you need RAG
- Internal docs, wikis, playbooks you want to search with AI
- Support automation using your product documentation
- Sales assistant pulling from CRM context
- Legal, medical, financial domains where hallucinations aren't acceptable
- Any case where the LLM needs fresh, private, or large-scale data
What we deliver
Embedding pipeline
Document ingestion (PDF, DOCX, HTML, Confluence, Notion, SharePoint), intelligent chunking, OpenAI/Voyage/Cohere embeddings, incremental updates.
Vector store
Pinecone, Qdrant, Weaviate, or pgvector — based on data sensitivity and scale. EU-region hosting available.
Hybrid retrieval
Vector similarity + BM25/full-text + metadata filters + re-ranking (Cohere Rerank, BGE). 3–4x relevance gains over pure vector search.
Production integration
LLM orchestration (LangGraph, Vercel AI SDK), citation tracking, error handling, monitoring (LangSmith), cost optimization.
Typical timeline and cost
Small RAG (5–10k docs, single source): 4–6 weeks, €15k–€35k. Mid-enterprise (multi-source, hybrid, RBAC): 8–12 weeks, €35k–€80k. Regulated/multilingual enterprise: custom scope.
Let's build your RAG pipeline
30-minute call to scope your data, use-case, and compliance. We close with a firm quote and a 4-week roadmap.
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