About Trailbit
Research-DrivenBitcoin Analytics
Built on published academic research. Purpose-built for students, researchers, and investigators who want to understand how blockchain analytics actually works.
Built on Published Research
Transparent methodology, reproducible analysis
Trailbit began as blockchain analytics research and grew into a full investigation platform. Every heuristic, risk rule, and clustering method in the engine is grounded in published academic work — documented, justified, and reproducible.
Compliance teams and investigators are increasingly required to explain their screening methodology in court filings and regulatory reviews. Trailbit makes that straightforward: every risk score and pattern detection is traceable to a documented heuristic with known confidence levels and false-positive characteristics.
The platform exists to make blockchain analytics accessible — giving students, researchers, and investigators the same caliber of analysis tools that were previously locked behind enterprise contracts.
Our Mission
What Drives Us
Accessible to Everyone
Enterprise analytics platforms charge six figures per seat. Trailbit delivers professional-grade analysis — 22 modules, 49 risk rules, multiple export formats — accessible to students, researchers, and independent investigators.
Continuous Research
Trailbit's analysis engine evolves with the threat landscape. New detection algorithms, updated watchlist data, and refined heuristics are informed by ongoing research at labs.trailbit.io, our dedicated blockchain analytics research lab.
Analytical Rigor
Every heuristic is grounded in published research. Every risk rule has documented thresholds and weights. Trailbit reports confidence levels alongside findings because real analytical work demands transparency about what the data does and doesn't tell you.
Trailbit's analysis engine uses heuristic methods and should be used as one component of a thorough investigation. Findings should be verified through additional sources before drawing investigative conclusions. For methodology details, see our published research at labs.trailbit.io.