advisoryaicontact
Trades & Service Companies

yourtechniciansshouldbefixingproblems,notfieldingphonecalls.

Trades companies scale on process — intake, dispatch, diagnosis, scheduling, invoicing. But most of those processes are still manual, phone-based, and dependent on individual knowledge. When the key person is unavailable, the system breaks down.

thescalingwall.

Every trades company hits the same ceiling. You can't grow past what your manual processes can handle. The front desk becomes a bottleneck, your senior techs spend half their time on phone calls instead of billable work, and institutional knowledge walks out the door when people leave.

  • Front desk staff overwhelmed by call volume they can't fully diagnose
  • Senior technicians pulled off billable work to handle intake and triage
  • Institutional knowledge trapped in the heads of your most experienced people
  • No visibility into patterns — same problems repeat because nobody tracks the data

thesystem.

AI that handles the intake, captures the knowledge, and surfaces the patterns — so your team focuses on the work that requires their hands and expertise.

Diagnostic intake

Customers walk through AI-guided troubleshooting before a human touches the ticket. Basic fixes recommended automatically, complex issues arrive fully scoped with the specific problem identified.

Automated ticket scoping & routing

AI categorizes, prioritizes, and routes service tickets to the right technician based on problem type, location, and skillset. No more manual triage bottlenecks.

Knowledge capture

Your senior technicians' expertise — the troubleshooting patterns, the diagnostic shortcuts, the institutional knowledge — captured into a queryable AI system that makes every technician as effective as your best.

Operational intelligence

Once intake is automated, the structured data feeds analysis. "What are the top 5 maintenance issues this quarter?" / "Which technician has the fastest resolution time?" — insights that were previously invisible.

alreadyinthefield.

We've worked with a mechanical contracting company that was already exploring AI tooling but needed the bridge between working demo and production-ready deployment. Trades and service companies are massively underserved by AI consulting — most AI firms target SaaS and enterprise. The trades are high-volume, process-heavy industries where intake and dispatch automation has immediate, measurable ROI.

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.