Making Blockchain Data Actually Useful

We've spent years figuring out how to turn messy blockchain transaction data into insights that finance teams can actually work with. No buzzwords, just practical machine learning applied to real problems.

2019 Founded

3.2M+ Transactions Analyzed

18 Team Members

Started With a Problem Nobody Wanted to Solve

Back in early 2019, a small trading firm in Ho Chi Minh City asked us to help them understand their blockchain transaction patterns. They were drowning in data but had no way to spot anomalies or predict network congestion.

We built something that worked. Word spread. More financial teams reached out with similar headaches—reconciliation nightmares, compliance blind spots, patterns they couldn't see in thousands of transactions.

So we kept building. Turns out there's a gap between what blockchain promises and what finance teams actually need to do their jobs. That's where we sit.

We're not here to revolutionize anything. Just to make blockchain analytics less of a headache for teams doing real financial work in Vietnam and beyond.

What Guides Our Work

These aren't corporate values we put on a wall. They're the things that actually shape how we build tools and work with clients.

Honest About Limits

Machine learning isn't magic. When our models can't do something reliably, we say so upfront. Better to acknowledge limitations than overpromise.

Built for Humans

Analytics tools are worthless if your team can't understand them. We design interfaces that finance professionals can actually navigate without a PhD.

Local Context Matters

Working in Vietnam means understanding local regulations, banking practices, and business culture. Cookie-cutter solutions from abroad miss too much.

Who Actually Does This Work

Small team. Mix of data scientists who know finance and finance people who learned data science. Works better than you'd think.

Most blockchain analytics platforms try to do everything for everyone. We focus on one thing—helping financial operations teams in this region make sense of transaction data. That narrow focus means we can go deeper where it matters.

Linh Bùi

Technical Lead, Data Science

How We Build Models

Start with actual client problems, not theoretical possibilities. Test on real transaction data. Iterate based on what finance teams actually need to see.

Working With Clients

Monthly check-ins, not annual contracts. If something isn't working, we adjust. Your feedback shapes what we build next.

Training & Support

We don't just hand over a dashboard. Initial training, documentation in plain language, ongoing support when you hit snags.

Staying Current

Blockchain protocols change. Regulations evolve. We continuously update our models and keep clients informed about what matters.

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