I take AI from whiteboard to regulated production, and I know what it costs to run.
I'm an AI engineer & solutions architect. Ex-Google, ex-AWS (11× certified), most recently leading GPU-PaaS and distributed-LLM infrastructure at Rafay. I advise, architect, deploy end-to-end, and operate cost-optimized production AI, at founding / fractional-CTO altitude.
the one that proves it
enterprise case study · anonymized
A document-understanding platform for a regulated Malaysian bank
Designed, built, and shipped an AI document-understanding platform inside a regulated, data-resident, human-in-the-loop environment, where the model is the easy part and everything around it (residency, auditability, a human who can always say no) is the hard part.
and I ship, repeatedly
AI automation platform for businesses: automates workflows, scales operations, and drives growth through intelligent agents.
Write a poem: it's painted as a text-free ink-wash scene, sealed, even sung.
Turn long video into clear, structured answers on demand: ask a question, get the exact moment and a summary.
StoryFlow breathes new life into your footage, reworking the narration so your story lands the way you meant it.
Turn a product photo and a brief into a finished campaign: a council of AI agents debates the angle, generates a keyframe and a 9:16 video, then hands back a distribution plan and an ROI hypothesis.
how I work
End-to-end ownership
Whiteboard to production to handover. The measure is whether the client can run it after I leave.
Cost is a design constraint
I track what every system costs to run, and optimize it. I do it for my own portfolio too.
Human-in-the-loop AI
In regulated settings the human is the control, not the inefficiency. Confidence-gated, auditable.