The People Behind Your Financial Clarity
We're not just another fintech startup. We're financial professionals who got tired of watching businesses struggle with expense categorization that should be simple.
Meet Our Core Team
Two professionals with very different backgrounds who discovered they shared the same frustration with traditional expense management. Here's how that frustration became our mission.
Kelverton Ridge
Lead AI Architect
I spent eight years building machine learning models for a major Bangkok bank, watching accountants manually sort thousands of transactions daily. The irony wasn't lost on me—here I was creating AI for credit risk assessment while finance teams three floors down were still using spreadsheets from 2018. After my third promotion and fourth migraine from seeing inefficient processes, I decided to solve the problem myself. Turns out, teaching machines to understand "office supplies vs. marketing materials" is trickier than predicting loan defaults, but infinitely more satisfying when you see someone's workday get easier.
Marcus Chen
Financial Systems Specialist
My accounting career started traditionally enough—Big Four firm, long hours, the usual suspects. But I kept noticing that our most successful clients weren't necessarily the ones with the biggest budgets. They were the ones who understood their spending patterns quickly enough to make smart decisions. After managing financial systems for manufacturing companies across Southeast Asia, I realized that speed of insight matters more than perfect precision. Most businesses don't need academic-level accuracy; they need clarity fast enough to act on. When Kelverton showed me his early expense categorization models, I knew we could build something that actually fits how real businesses operate.
Our Shared Journey
Met at a Bangkok fintech meetup where we both complained about the same thing—brilliant AI being wasted while basic business problems remained unsolved.
Started building our first prototype using real transaction data from Marcus's former clients (with permission, of course). Learned that "restaurant expense" has about fifteen different variations.
Launched our MVP with five Bangkok-based businesses. Discovered that our system worked better when we stopped trying to be perfect and started being useful.
Now serving businesses across Thailand, still maintaining our original promise: make expense categorization simple enough that you can focus on growing instead of sorting.
What Drives Our Work
These aren't corporate values from a boardroom brainstorm. They're the principles we developed after watching too many businesses struggle with unnecessarily complicated financial processes.
Speed Over Perfection
We've seen businesses miss opportunities because they spent weeks getting their expense reports "perfect." Our AI gets you 95% accuracy in minutes, which beats 100% accuracy three weeks later.
Practical Intelligence
Our machine learning models are trained on real business scenarios, not academic datasets. They understand that "Grab to client meeting" is a transport expense, even if the receipt just says "ride fare."
Transparent Operations
When our system makes a categorization decision, you can see why. No black box algorithms—just clear logic you can verify and adjust when your business needs change.