About
Engineering judgment, applied to the practice of law.
I build production AI systems. Legal documents are the domain I've chosen — high-stakes, deeply structured, unforgiving of hallucination, and one of the most valuable places a well-engineered AI system can be deployed.
Kevin Luzbetak
Data & Machine Learning Engineer · Los Angeles, California
I hold a Master's degree in Artificial Intelligence (2014) and have spent the intervening years designing and shipping data and ML infrastructure at scale — ETL pipelines, vector databases, retrieval-augmented generation systems, and production NLP.
My current focus is legal document intelligence: the careful application of leading cloud reasoning models and open-weights local inference to contracts, regulatory filings, and discovery material. The goal is not to replace attorneys — it is to give them a first draft worth reviewing, with citations, confidence scores, and an audit trail.
If you represent a firm, corporate legal department, or legal-tech venture that needs hybrid AI architecture done right — privacy preserved, costs controlled, and outputs grounded in source documents — I'd like to hear about the problem.
Experience
Where the work has come from.
Principles
How I approach the work.
Ground every answer in a source.
No output ships without a citation. If the model can't point to the paragraph, the finding doesn't make it into the report.
Let the attorney decide what leaves the building.
Sensitive matters run against local models. Cloud inference is an option, not a default. You see the routing policy; you set it.
Measure it, or it isn't working.
Every deployment ships with evaluation harnesses — gold-set precision, recall, and drift monitoring in production — not vibes.
Respect the existing workflow.
The system plugs into how your team already reviews contracts. It does not demand that you rebuild the review process around it.