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I build the AI-powered part of a product — the RAG pipeline, the agent workflow, the vision feature — and I build it to survive contact with real users. Every project on this page ships with the same instinct: know when the model is right, and have a real answer for when it's wrong.
Teams shipping an AI feature inside a product that already exists — a Shopify app, a mobile app, an internal tool — who need it production-ready, not a demo. I'm most useful where a wrong answer costs something: regulated or trust-sensitive work, anything customer-facing, anything where "the model said so" isn't good enough on its own.
Small-to-mid SaaS teams adding an AI feature without hiring a whole ML team for it. Founders who've built the product and need the AI part done properly. Anyone in healthcare, clinical, legal, or another regulated space who wants AI but can't afford it hallucinating.
Clinical trial data doesn't tolerate an unverifiable claim — every number needs a source, every change needs a reason. I build AI systems on the same rule: it's why Ayuti cites its sources, why Mersail won't send without approval, why Brezza Viva refuses to generate rather than risk it.
An AI version of me, running live. Ask it anything a recruiter would ask — background, skills, why I'm moving into AI engineering.
A 30-minute intro call — I'll walk you through my projects and how my clinical data background translates to AI engineering.