Start with the work
Before tools or models, map the documents, calls, approvals, spreadsheets, and handoffs.
From law to enterprise salesto AI systems.
I like AI agents because they reveal the real operating system: trusted documents, handoffs, review loops, and decisions that should stay human. I'm exploring how AI-native teams store context, delegate tasks, review outputs, and build durable trust around agents.
"Useful automation starts after the workflow is legible: who owns it, where it begins, where the handoffs happen, what good looks like, and how you'll know when it breaks."
GTMProductAI Workflows
LocationSingapore / San FranciscoFocusAI-native workStatusOpen to roles
How I Started
I studied law at Cambridge and wrote my dissertation on machine learning in sentencing. That gave me the first lens I still use for AI work: the hard part is not just the model, but evidence, judgment, process, and the institutions around it.
Boxo and Airwallex taught me the enterprise side: selling technical products, mapping stakeholders, removing risk, and seeing how messy workflows become buying decisions. I learned to translate between operators, buyers, product teams, and technical constraints.
Salescraft was the hands-on turn: I built AI GTM systems for startups and learned how recurring edge cases become reusable rules. That led me to San Francisco, where I worked with Sample Healthcare as a Forward Deployed Strategist on patient-intake and prior-authorization workflows. I carried that into Sunder, where I now build source-backed systems across legal documents, CRM, company memory, and order intake.
How I Work
Stay close to operators
Useful systems come from watching where people actually get stuck, not from guessing in a vacuum.
Make evidence visible
Citations, confidence, audit trails, and escalation paths matter when outputs affect real work.
Translate across teams
I move between customer language, commercial reality, product shape, and technical constraints.
What I'm Looking For
I am looking for roles close to real users and consequential decisions: ambiguous inputs, commercial outcomes, and enough operational depth that product judgment matters. I am especially interested in San Francisco teams building AI Healthcare, applied operations, or GTM systems.