RAG demos look great. Production is where they break.
I build document AI systems that survive production — or diagnose why existing systems fail.
Investment firms, legal ops, medical data, enterprise platforms — complex tables, scanned PDFs, entity-heavy contracts, inconsistent structure. If the reliability of your automated systems is mission-critical, that's where I work.
About me
I'm Halyna — I've spent 17 years making search and extraction systems work on real-world documents. Long before LLMs, I was building information extraction pipelines for legal, HR, and government domains, learning what makes retrieval succeed or fail at a fundamental level.
That foundation shapes everything I do today. When I work with modern LLM systems I bring deep understanding of chunking strategies, entity resolution, hybrid search tradeoffs, and document structure analysis. That specialized "under-the-hood" knowledge enables companies to move reliably past the demo phase into high-accuracy production.
My approach
Build evaluation into the architecture from day one. I move teams away from "vibe-based" testing and toward quantifiable benchmarks. You gain certainty on exactly where to trust the output before committing to scale.
Diagnose failure modes. When production systems break, I perform forensic analysis on the retrieval and extraction pipeline to identify specific failure modes. By focusing on evidence-based fixes and deterministic testing, improvements actually stick. The goal is to move beyond the binary "does it work?" to a clear map of system authority.
Recent work
Investment Data Extraction (VC Fund, 12+ months): Automated complex extraction replacing a 3-7 person manual workflow. Built multi-stage LLM architecture achieving 94% accuracy across 690 complex entities with zero hallucinations. System designed to report "not found" rather than invent answers.
RAG Evaluation Infrastructure (Enterprise Search): Built systematic measurement for an enterprise search assistant. Replaced noisy metrics with calibrated LLM-as-a-judge framework and CI/CD regression testing. Transformed ad-hoc testing into repeatable, automated evaluation.
Medical Document Intelligence (Healthcare, ongoing): Building extraction and evaluation system for clinical lab results and doctor reports — handling inconsistent formats and table complexity.
Technical foundation
Information Extraction | RAG Evaluation & Reliability | Document Intelligence | LLM-as-Judge Frameworks | Hybrid Search | Python/FastAPI | Pydantic | Java | Elasticsearch/Lucene
Why work with me?
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Deep IR Fundamentals
Pre-RAG expertise in enterprise search means I understand why retrieval fails at a fundamental level — not just "call the API and hope."
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Extraction + Evaluation, Integrated
I don't just build pipelines. I build the measurement systems alongside them, so you know what works before you scale.
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Trusted Long-Term Partner
100% retention rate. My clients stay because I deliver systems that work — and honest assessments when they won't.
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Production-Grade Reliability
94% accuracy, zero hallucinations, CI/CD regression testing. Systems built for real documents, not demo datasets.
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Let's have a virtual coffee together!
Want to see if we're a match? Schedule a free intro call to discuss your AI challenges and explore how we can work together.