Production-ready AI. From prototype to trusted system.
I help teams turn promising AI prototypes into systems they can measure, trust, and improve.
- Build the right benchmark before you scale
- See where the system fails and why
- Validate changes before they hit production
Prototyping with AI is exciting. We have all tried and seen how rewarding it can be. The harder part is knowing what is ready to ship, what is still fragile, and how to improve it without guessing.
I build the evaluation harness around your solution — benchmark, metrics, error analysis, and validation loop — so your team can understand the system, operate it confidently, and extend it without fear.
Proof from a recent project: 94% accuracy across 690 complex entities · zero hallucinations · full source traceability
For teams working with: scanned PDFs · complex tables · contracts · entity-heavy workflows · high-stakes document operations
Build trust before you scale
Most document AI systems don't fail because nobody can code them. They fail because teams cannot clearly see:
- what "good" looks like for their use case
- whether the system is actually improving
- which errors matter most
- whether a change made things better or just different
That is the gap I close.
I help you put a working measurement and improvement system around your AI solution, so progress becomes visible, experiments become safer, and scaling stops feeling like a leap of faith.
What you leave with
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A benchmark built around your real documents
The right fields, the right structure, and enough coverage to be decision-useful.
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Metrics you can actually operate with
Defined, calibrated, and automated.
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Error analysis that points to action
Not just "the model is wrong," but where the failure comes from and what to change next.
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A repeatable loop your team can own
Change, test, compare, iterate. Not a black box, not a one-off demo.
Curious whether this fits your situation? Start with a free discovery call. We'll figure out the right approach together.
Proof
I work on document AI where reliability is not optional.
- 94% accuracy across 690 complex entities
- Zero hallucinations in high-precision extraction
- Full source traceability for every answer
- Experience with scanned PDFs, complex tables, contracts, and entity-heavy documents
- Background in search, information extraction, and document structure long before LLMs
Typical use cases: Information extraction from complex documents · Retrieval and answer grounding over enterprise content · Evaluation harnesses for document AI systems · Reliability work before scaling pilots into production · Hardening workflows that currently depend on manual review
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.
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.
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.