RAG system on OpenAI and Upstash Vector with 30 language support. Includes streaming chatbot, hybrid search (semantic + keyword), AI ticket classifier, and
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Iurii Uses: #RAG
1 project · 2 articles · 1 review
Retrieval-Augmented Generation (RAG) enhances language model responses by fetching relevant context from a vector database before generation. It grounds AI answers in your own documents rather than relying on training data alone.
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· 11 min
Add AI to an existing product without a rebuild. Three integration patterns, how to pick the right one, and what production-ready AI actually demands.
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· 9 min
RAG chatbot for e-commerce: resolve 70% of support queries across 25 languages — ingestion pipeline, hybrid search, confidence thresholds, and streaming UI.
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Our support inbox was drowning — most tickets were repeat questions our docs already answered, but customers weren't finding them. Iurii built us a retrieval-based assistant on top of our existing knowledge base in about three weeks. It answers around 60% of incoming questions correctly and hands the rest to a human with full context already attached. What I appreciated most was that he was honest about where LLMs would fail before we started, so there were no surprises later.
Anna Lindberg 🇸🇪
Head of Operations
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Service: ai integration
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