advisoryaicontact
Support Automation

yourcustomersgetanswersinseconds.yourteamgetsticketsthatarealreadydiagnosed.

Front desk takes a call. Relays vague info. A tech investigates from scratch. Calls back. Every step adds delay and loses context. AI diagnostic intake eliminates the loop.

theloop.

Every service company runs the same broken workflow. A customer calls with a problem they can't fully describe. Your front desk captures half the story. A technician or specialist starts from zero, asks the same questions again, and burns time on diagnosis before they can even begin solving.

  • Front-line staff spend hours relaying information they don't fully understand
  • Technicians waste skilled time on intake instead of resolution
  • Customers wait longer because every handoff loses context
  • Repeat calls for the same issue because the first interaction didn't capture enough detail

thefix.

AI handles the first interaction. Your team handles the work that actually requires them.

AI diagnostic intake

Walks customers through targeted questions to narrow down the problem, recommends basic fixes, and submits a fully scoped ticket to your team. By the time a human touches it, they know exactly what they're dealing with.

Multi-channel deployment

Phone, web, chat — the same diagnostic logic deployed wherever your customers reach you. Built with VoiceFlow, custom integrations, or whatever fits your stack.

Structured ticket output

No more vague "something is broken" tickets. Every AI-processed intake includes the specific problem identified, steps already attempted, and recommended next action for your team.

Measurable ROI from day one

We instrument everything: resolution time, first-contact resolution rate, ticket quality score, cost per interaction. You know exactly what the automation is worth.

nottheoretical.

We run a version of this on our own site — the diagnostic intake chatbot on rockwellgroup.xyz/ai uses the same conversational scoping logic to qualify and route inbound leads. We're actively building a customer-facing deployment for other companies with similar needs. The underlying architecture is proven. The client deployments are underway.

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.