I believe Fair Pay
is Profitable.
By “Fair” I mean consistency in practice and transparency of philosophy.
When those break, use the FAIR + SIGNS frameworks to check and mend the system.
I work on pay, equity, benefits, and the systems around People.
Lately, that also means trying to build AI tools to help.
Try the tools I've built for free.
Built out of curiosity. Sharing to help.
Free, no strings. Each one solves a real problem someone I know ran into. Some are live. Some are still cooking.
FLSA Classification Tool
Supports more accurate FLSA role classification before mistakes become expensive.
Canada Employment Standards Classification Tool
Helps People Ops review Canadian overtime, hours-of-work, and worker-status classification.
Compensation Philosophy Builder
Helps Total Rewards teams draft, critique, and export an executive-ready compensation philosophy package.
Two frameworks for when rewards decisions feel a little too convenient.
FAIR looks at the system. SIGNS looks at the signal. Most pay problems sit in one of those two places. Often both.
FAIR is how to diagnose whether a rewards system actually holds up.
FAIR checks four things: Friction, Alignment, Impact, Return.
(opens in a new tab)SIGNS is how to diagnose what a single decision is actually communicating.
SIGNS checks five things: Story, Intent, Gap, Norms, Signal Strength.
(opens in a new tab)Three continents.
One operating system.
I started 25 years ago as a server-side web developer in Sri Lanka. Then New Zealand. Now New York, running Total Rewards at a Series E digital asset infrastructure company. The career arc is unusual. So is the perspective.
Most nights you'll find me vibe coding with my husband, building small apps and testing LLMs.
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A few ways to say hello.
LinkedIn is where I post when I have something worth sharing. ADPList is better if you want to talk through a real problem. If your CRM labeled me “a warm lead,” it is not warm. No sales pitches please.