RAG system on OpenAI and Upstash Vector with 30 language support. Includes streaming chatbot, hybrid search (semantic + keyword), AI ticket classifier, and
Iuriiย Builds: AI Integration
Add AI features to your existing product. Whether it's a chatbot, document processing, or smart recommendations โ I'll plug it in cleanly.
Based in Finland (UTC+2) ยท VAT-registered FI29845875 ยท Respond within 24 hours
Recent client work
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Our support inbox was drowning โ 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 ๐ธ๐ช
Head of Operations
Topics
There's a lot of AI hype and a lot of AI waste. I focus on AI that does something genuinely useful: answering customer questions accurately, processing documents automatically, extracting structured data from unstructured input.
I've built RAG systems for multilingual e-commerce (30+ languages), document analysis tools for fintech, and AI chatbots that actually stay on-topic. I know what works in production and what looks good in demos but falls apart with real data.
I work with OpenAI and Anthropic's Claude. I'll recommend the right model for your use case โ not the most expensive one.
What's included
- OpenAI (GPT-4o) and Claude integration
- RAG (retrieval-augmented generation) with vector search
- AI chatbots with conversation memory and context
- Document analysis, extraction, and classification
- Embedding pipelines and semantic search
- Prompt engineering and output validation
How I approach this
01
Define what 'good' looks like
What should the AI do? What's an acceptable error rate? What are the failure modes you can't tolerate?
02
Choose the right approach
RAG, fine-tuning, or pure prompting? The right choice depends on your data, latency requirements, and budget.
03
Build and evaluate
Build the pipeline, then evaluate it against real examples. Iteration is built into the process.
04
Deploy with guardrails
Output validation, rate limiting, cost monitoring. AI in production needs more guardrails than most integrations.
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More AI Integration reviews
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We wanted to add an AI feature that turns messy user notes into structured records, but our first attempt returned unpredictable JSON that broke the app half the time. Iurii rebuilt it using structured outputs against the OpenAI API with proper validation, so the data is always shaped the way our database expects. He also added a fallback path for when the model is unsure instead of letting it guess. It's been running in production for a month with no manual cleanup.
Bram de Vries ๐ณ๐ฑ
Product Lead
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We process thousands of supplier invoices a month and the team was keying them in by hand. Iurii built a pipeline that reads each document, extracts the fields we care about, and routes anything that doesn't match a known template to a human โ with a confidence threshold so nothing gets posted automatically that the model wasn't sure about. He was blunt up front that we shouldn't try to automate the low-confidence cases, and that honesty is exactly why finance trusts the system. It's handled around 85% of the volume for two months now with no bad postings we've had to reverse.
Lena Brandt ๐ฉ๐ช
Head of Product
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Tell me what you're building or stuck with. I'll reply within 24 hours.