Independent venture research · Melbourne · Australia ↔ Asia
I investigate emerging consumer behavior across physical AI, neurodiversity, and cross-border transitions—then test whether it can compound through workflow, economics, and fund fit.
Selected work / 2026
01—04Each piece makes the evidence boundary visible: what is verified, what is company-reported, what is modeled, and what remains unknown.
Public research · 10-company first-call list
Which founders are building the trust layer between arrival and participation?
Independent IC case
Can a narrow behavioral wedge become a venture-scale platform?
Investment thesis · 30-company map
Can international students become the entry wedge for a larger migrant consumer platform?
Field research + open Excel model
Does visible demand translate into repeatable store-level economics?
How I form a view
Look for a moment when an old interface, institution, or habit stops serving a concentrated user group.
Identify the workflow, payer, supply, trust, retention, and operating constraints behind visible adoption.
Connect evidence to unit economics, ownership, dilution, exit scenarios, and contribution to fund return.
State the initial belief, disconfirming evidence, revised view, and the next milestone that would change it again.
About / investment lens
I am a Monash University finance student in Melbourne, building an independent body of venture research at the intersection of Australia, Asia, emerging technology, and changing consumer behavior.
I am drawn to users who are poorly served by default systems: people rebuilding life across borders, neurodivergent consumers adapting tools to their cognition, and young people creating new physical and digital routines. These groups often reveal a product wedge before the market has a clean category name.
Cultural context helps explain adoption; it cannot prove an investment. I therefore pair field observation and public research with bottom-up models, evidence labels, and explicit fund-return tests. When the model contradicts the story, the story changes.
Research integrity
01Company-reported data is never presented as independently verified fact.
02Model inputs are disclosed separately from external evidence.
03Public case studies are independent and not commissioned by the companies or funds discussed.
04A revised answer is treated as an analytical output, not a failure of conviction.
Melbourne / Australia
Finance training, an Australia–Asia perspective, and work samples built to make judgment inspectable.