advisoryaicontact
Compliance Automation

compliancethatwatchessoyourteamdoesn'thaveto.

Manual transaction review, STR narrative writing, document checking, audit trail maintenance — all high-volume, high-stakes, and mostly repetitive. AI handles the volume. Your compliance team handles the judgment.

themanualgrind.

Compliance teams drown in repetitive, rules-based work. Every transaction needs review. Every suspicious pattern needs a narrative. Every document needs checking against regulatory criteria. The work is critical but most of it follows the same patterns — patterns that AI can execute faster, more consistently, and without fatigue.

  • Transaction review backlogs that grow faster than the team can process
  • STR narrative writing that takes hours per filing despite following the same structure
  • Manual document validation against criteria that rarely change
  • Audit trail maintenance that consumes senior compliance time on logging instead of analysis

thesystem.

Deterministic compliance logic with AI assistance — never the other way around. The rules are hard-coded. The AI handles the volume.

Transaction monitoring

AI-powered pattern detection that flags suspicious transactions using deterministic scripts — not hallucinated outputs. Wallet scoring integration with ScoreChain and BitRank for crypto compliance.

STR narrative generation

Automated Suspicious Transaction Report narratives with regulator-aligned structure. AI drafts, humans review. Same quality, fraction of the time per filing.

Document validation

Automated checking of documents against defined compliance criteria. Every validation logged, every decision traceable, every audit trail maintained without manual effort.

PII-safe architecture

PII anonymization before AI processing — age ranges instead of birthdates, postal prefixes instead of full addresses. Deterministic compliance logic layered with AI assistance, never the other way around.

compliance-native.

This isn't a generic AI team learning compliance on your dime. Luke holds a CAMS certification and built Comply+, a FINTRAC automation platform. He's written the compliance logic, filed the reports, and understands what regulators actually examine. That domain expertise is baked into every system we build — from PII anonymization architecture to regulator-aligned narrative structures.
CAMSCPACFA

theteam.

brings CPA, CFA, and CAMS credentials to AI strategy — he understands the business problems before reaching for the technology. brings deep engineering experience in production AI systems. Together, that’s the full stack: someone who knows what to build and someone who knows how to build it.
Luke ThibodeauCPA · CFA · CAMSFounder and CEO

Luke is a finance and compliance operator who works hands-on with bitcoin-native companies, fintechs, and MSBs as a fractional CFO and CCO through Rockwell Advisory Group. As former CFO & CCO of Bitcoin Well (TSXV:BTCW), he built and ran the finance, compliance, and governance functions across a publicly listed, multi-entity crypto company from the ground up.

Today he brings that same operational depth to clients navigating treasury management, FINTRAC compliance, financial reporting, and corporate governance. Whether it's standing up a compliance program for a new MSB, managing multi-entity consolidations, or advising on AI strategy through Rockwell AI, Luke operates as an embedded member of the team rather than an outside advisor. He also founded Comply+, a RegTech platform automating FINTRAC reporting for Canadian MSBs, born directly from the pain points he encountered building compliance workflows by hand.

Daniel FrazeeHead of Integration

Daniel is a technologist and operations leader serving as Head of Integration at Rockwell AI, helping companies turn AI strategy into working systems. As COO of Próspera, he built and scaled operations across a complex, multi-jurisdictional organization, managing everything from employment infrastructure to day-to-day execution across distributed teams.

Before that, he spent four years at Chevron as an IT professional, working inside the kind of large-scale enterprise systems where reliability and process discipline aren't optional. That combination of big-company engineering rigor and startup-stage operational ownership shapes how he approaches integration: grounded in real constraints, focused on outcomes, and built to hold up in production. Daniel holds a B.S. in Computer Science from Texas A&M University, with deep experience across software development, machine learning, and operations management.