Pattern Recognition That Actually Makes Sense
Blockchain analytics isn't about crunching numbers until something sticks. We spent three years building machine learning models that spot meaningful patterns in transaction flows—the kind that help you understand what's really happening beneath the surface.
See How We Work
Three Problems We Kept Running Into
Before we built our own tools, we faced the same frustrations everyone else did. So we fixed them.
Too Much Noise
Standard blockchain explorers show you everything. Which is basically the same as showing you nothing when you're trying to find suspicious activity. Our filters learned what matters—and what doesn't.
Missing Context
A wallet address transfers funds. Okay, but why? Our models look at behavioral patterns across time, not just isolated events. Context changes everything when you're assessing risk.
Slow Detection
By the time traditional analysis flags something, the money's gone. We built real-time monitoring that spots anomalies as they develop, not after the fact.
From Manual Checks to Automated Intelligence
Early 2023: The Breaking Point
We were manually reviewing transaction patterns for a compliance team. Eight hours a day, spreadsheets everywhere. After missing a critical pattern that our junior analyst caught by accident, we knew something had to change.
Current State: October 2025
Same team now processes 50x the transaction volume with better accuracy. Not because they got smarter—because the machine learning does the heavy lifting. They focus on investigation, not data sorting.
Thao Linh Bui
Lead Analyst
Who's Actually Building This
Our team came from different places—financial compliance, data science, cybersecurity. What we share is experience with systems that fall apart when they matter most.
Thao spent five years at a regional bank watching fraud detection tools generate false positives. She knew the math could work better. So we built models trained on real transaction behaviors, not theoretical scenarios.
Network Analysis That Scales
We map relationships between wallets, exchanges, and contracts. Graph theory meets machine learning to show you the full picture.
Custom Training for Your Context
DeFi looks different than payment processing. Your risk profile is unique. Our models adapt to what matters in your specific environment.
Transparent Methodology
Black box AI is useless for compliance. We document why the system flagged something so you can explain it to regulators.