Taras Z.

If you have internal documents, tickets, or product data, I build AI agents and RAG systems that retrieve the right evidence, cite it, and take action - with no hallucinations and lower token costs. I'm a PhD in NLP/ML and sole inventor on two US patent applications in agentic AI (networked personal AI agents, and sycophancy-resistant evaluation of AI trust). I don't just use RAG and agent frameworks - I patent the methods behind them, which means the systems I build are grounded in original research, not just off-the-shelf wiring of LangChain components. Here is what I deliver: 🔹 RAG done right - chunking strategy, metadata, retrieval tuning, reranking, query rewriting, and citation-grounded answers with guardrails against hallucination 🔹 Agentic workflows - hierarchical (manager to specialists), parallel, and sequential pipelines using LangChain, LangGraph, or CrewAI 🔹 Tool-using agents - API/tool calling, safe execution, structured outputs, and robust fallback behavior 🔹 Long-context optimization - hierarchical summarization, compression, and selective retrieval that cuts token cost while improving stability on large inputs 🔹 Evaluation & reliability - benchmarks, regression tests, error taxonomy, confidence routing, and human-in-the-loop review Top Rated Plus with a 5 rating across 30 completed projects and 3,000+ hours on Upwork, plus two US patent applications (2025-2026) in agentic AI. Typical client outcomes include higher answer quality with grounded citations, lower inference costs through routing and compression, and production systems with measured accuracy and predictable failure modes. Share your data shape and target use case, and I'll propose the fastest path to a reliable RAG or agent system with measurable milestones. Message me and I'll respond within a day.