Volodymyr L.

I build AI features that survive production - not demos. Most teams can get an LLM prototype working in a weekend. What breaks is everything after: costs that scale wrong, outputs that drift, latency that kills the UX, and no way to tell whether quality is getting better or worse. That's the part I own. What I do RAG and semantic search over your documents, knowledge bases, and internal data LLM AI Agent and chatbot for support, onboarding, and internal tooling AI automation pipelines - enrichment, routing, summarisation, lead scoring Cost and latency engineering - right model per task, caching, streaming, evals so quality stays measurable as you ship Recent results Built a Claude-powered content pipeline: YouTube video to publish-ready blog, X, and LinkedIn output. Cut end-user content work 60% and saved clients 20+ hours/week AI lead scoring and automated follow-ups inside a custom CRM - 60% less sales admin work Content automation producing 500+ SEO articles/month, saving $4,000/month AI video editing dashboard, currently 418 hours in and ongoing Why the AI work holds up 12+ years of full-stack engineering before AI was the headline. I've run multi-tenant SaaS, payment flows, and APIs at 10M+ monthly requests across platforms serving 320K+ users in 27+ countries at 99.9% uptime. AI Agent features fail in production for ordinary engineering reasons - bad error paths, no observability, unbounded costs. I've been fixing those for over a decade. What separates an AI integration that works in testing from one you can trust in production is engineering, not prompting. I bring eval suites in CI so quality stays measurable as you ship, structured output validation, cost and latency engineering (right model per task, caching, streaming), and the error handling and observability that LLM features usually lack. That discipline comes from 12+ years of full-stack work before AI was the headline - multi-tenant SaaS, payment flows, and APIs at 10M+ monthly requests with 99.9% uptime. Stack LLMs: OpenAI API, Claude, Gemini · LangChain, LangGraph · RAG, vector search, embeddings Backend: Node.js, NestJS, Python, TypeScript, PostgreSQL, Redis Automation: n8n, Node-RED · Cloud: AWS (Lambda, ECS, RDS, S3), Docker, CI/CD How I work Short discovery first - goals, users, constraints. Then architecture and a phased plan (MVP → v1 → iterate), shipped in small reviewable steps. I reply within 4 hours, document decisions that matter, and stay available after launch. 100% Job Success, Top Rated Plus, 3,028 hours on Upwork. Good fit if you have an AI integration feature that works in testing but you don't trust in production yet - or an LLM idea you need turned into something real in weeks, not quarters. Send me a short note about your product and constraints. I'll come back with a realistic plan and the questions that matter.