We Build Models That Actually Work
Started in 2019 when three data scientists got tired of watching businesses make decisions based on gut feeling instead of patterns hidden in their own numbers.
Financial modeling isn't just math for us—it's about turning messy data into clarity. We've spent six years helping businesses in Vietnam understand what their numbers are really saying. No jargon, no black boxes. Just models that make sense and predictions you can trust.
How We Got Here
Back in 2019, we were working at different fintech companies across Hanoi and Ho Chi Minh City. Same frustration everywhere—leadership wanted data-driven decisions but didn't trust the models they had. Can't blame them. Most models were either too complicated to explain or too simple to be useful.
So we started meeting after work, building something different. Models that business people could actually understand. Predictions that came with context, not just numbers. By mid-2020, we had our first client—a manufacturing firm in Binh Duong that needed cash flow forecasting they could stake their expansion on.
That project taught us everything. The model worked, sure. But what mattered more was helping their finance team understand why it worked. Teaching them to spot when the model needed adjusting. Making them part of the process instead of just consumers of output.
We're not trying to replace human judgment. We're trying to give it better tools. Numbers only mean something when people know what to do with them.
The People Behind the Models
We're small on purpose. Every project gets attention from people who've been doing this for years, not junior analysts following a playbook.
Damir Korhonen
Lead Data Scientist
Spent eight years building risk models for banks before joining us. Obsessed with making machine learning interpretable. Will explain neural networks using coffee shop analogies until you get it. Originally from Finland, moved to Vietnam in 2017 and never looked back.
Vesna Tomić
Financial Systems Architect
Background in corporate finance and systems engineering. Bridges the gap between what finance teams need and what data can deliver. Has a gift for spotting problems in financial processes that everyone else just accepts as normal. Joined us in 2021 after five years at a regional investment firm.
What Working With Us Actually Looks Like
We don't do cookie-cutter solutions. Every business has different data, different goals, different constraints. Here's how we figure out what you actually need.
Why Projects Succeed or Fail
Been doing this long enough to know the patterns. Projects work when clients understand they're buying expertise, not magic. When they're willing to clean up their data infrastructure. When they give us access to the people who actually use the financial systems daily.
Projects struggle when expectations are unrealistic. When data quality is worse than anyone admits. When stakeholders want answers but won't invest time in understanding the questions. We're upfront about this stuff during initial conversations because nobody benefits from starting something that's set up to disappoint.
- We don't promise perfect accuracy—financial markets are messy and patterns shift
- We do promise models that improve decision-making and adapt as conditions change
- We're selective about projects because doing fewer things well beats doing everything poorly
- We stay involved after launch because models need maintenance and your team needs support
Let's Talk About Your Data
If you're sitting on financial data you're not using effectively, or trying to make predictions with tools that don't quite fit, we should talk. First conversation is just that—a conversation. No pitch, no pressure. Just figuring out if what we do matches what you need.
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