I take AI from promising demo to a system your organization actually relies on.
Shipped work and two projects ready for the right partner to bring to production.
Problem Organizations accumulate institutional knowledge that lives in people's heads, and disappears when teams change or people leave.
Solution An AI-powered organizational memory layer built on a proprietary semantic addressing engine that directly indexes raw markdown. Teams query years of accumulated context in seconds.
Problem Clinical workflows demand reliable, auditable outputs. Standard LLM responses introduce ambiguity that is unacceptable in medical contexts.
Solution A deterministic AI assistant with schema-enforced, structured outputs. Every recommendation is traceable, predictable, and safe to act on in a clinical setting.
Problem Generic AI content lacks brand consistency and requires heavy editing before it is usable, defeating the purpose of automation entirely.
Solution A content generation system where brand voice, structure, and tone are enforced at the model level, not patched in post-production.
The gap between a demo and a shipped product is where I work.
Connecting large language models to real systems: databases, APIs, internal tools, so the AI has context and the system has intelligence.
Engineering structured, predictable AI outputs for high-stakes environments. Schema-enforced, auditable, and safe enough to build business logic on top of.
From backend pipelines to frontend interfaces, building the whole product, not just the AI layer. Web tech and LLMs wired end to end.
Moving fast from concept to deployed product. Not just prototypes. Real apps with real users, running on real infrastructure.
Most AI projects fail in the gap between a working demo and a reliable product. This is how I close it.
Before any architecture decision, I map the actual problem: what does the organization need to be true, what breaks if AI gets it wrong, and where does determinism matter. The technical approach follows from that, not the other way around.
A demo that surprises people is not the goal. A system that outputs the right structure, every time, under real conditions, is. Schema-enforced outputs, tested edge cases, and a clear handoff plan are built in from the start.
Delivery means deployed, documented, and your team understands how to operate it. I do not disappear after launch. I make sure the product is yours to run.
Tell me what you are building. I will tell you if I can help, and how.
ulubis98@gmail.com