advisoryaicontact
Financial Automation

month-endcloseinhours,notweeks.sameaccuracy,fractionofthetime.

Bookkeeping, bank reconciliation, expense categorization, and period-end reporting are high-volume, rules-based tasks that consume skilled accounting time disproportionately. AI handles the volume. Your team handles the review.

thebottleneck.

Your most expensive accounting resources spend their time on the least strategic work. Categorizing expenses, matching bank transactions, reconciling accounts, assembling reports — it's all necessary, it's all rules-based, and it all takes too long.

  • Month-end close stretches into weeks because every step is manual
  • Senior accounting time consumed by data entry and categorization instead of analysis
  • Off-the-shelf bookkeeping products don't handle complex or multi-entity structures
  • Reconciliation exceptions pile up because the volume exceeds the team's capacity

the80/20.

We analyze all finance tasks, identify the repetitive high-volume work, and automate in sequence — with human review checkpoints on every output.

Expense categorization

AI handles the volume of transaction categorization with pattern learning from your historical data. The same rules your bookkeeper applies manually, executed consistently across thousands of transactions.

Bank reconciliation

Automated matching of bank transactions to book entries. AI handles the clear matches, flags the exceptions, and your team reviews only what actually needs human judgment.

Period-end reporting

Month-end and quarter-end reporting workflows that run in hours instead of weeks. Same accuracy, fraction of the time — with human review checkpoints built into the process.

System integration

Connects to QuickBooks, Google Sheets, and your existing financial systems. No platform migration required — AI layers onto what you already run.

builtbyaCPA.

This isn't an AI team guessing at accounting. Luke is a CPA and CFA with deep understanding of what controllers actually need and what auditors actually accept. That means the automation is built around real accounting workflows — proper categorization hierarchies, reconciliation logic that handles exceptions correctly, and reporting formats that pass review. For companies whose needs don't fit an off-the-shelf bookkeeping product, this is the custom alternative.
CPACFA

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.