advisoryaicontact
Agent Development

customaiagents.builtforyourworkflows.runninginproduction.

Not chatbots. Not demos. Agents that connect to your existing systems, operate within defined security boundaries, and handle real work — deployed and monitored in production.

beyondthedemo.

Everyone has seen the AI demo. The problem is getting from a demo to a production system that runs reliably, connects to your actual tools, respects your security requirements, and handles edge cases without human intervention.

  • Off-the-shelf chatbots can't access your internal systems or business context
  • Enterprise AI firms charge $500K+ for deployments that take 6-12 months
  • Internal teams build proof-of-concepts that never make it to production
  • No visibility into cost, accuracy, or reliability once agents are running

whatwebuild.

Production AI agents that connect to your real systems and do real work.

MCP-based architecture

Agents connect to your existing systems via Model Context Protocol — your CRM, databases, project tools, and internal APIs. Each connection is scoped with explicit security boundaries and read-only access patterns.

Model routing & fallback chains

Production agents need reliability, not just capability. We build model routing chains — from high-capability models for complex reasoning down to fast models for simple tasks — with automatic fallback when primary models are unavailable.

Production security

PGP heartbeat monitoring, Tailscale VPN for database access, read-only query patterns, and defined security boundaries. These agents operate within the constraints your business requires.

Instrumented from day one

Every agent ships with latency tracking, accuracy measurement, cost per interaction metrics, and usage dashboards. You make the scale decision based on hard data, not hope.

productiontrackrecord.

We've deployed multi-agent systems in production — Company Oracle, Chief of Staff Agent, Compliance Monitor, SEO Intelligence Agent, and more. Our agent architecture handles orchestration across multiple models with automatic routing, security monitoring, and cost controls. We target the gap between companies that need production agents and the enterprise firms that charge half a million to deliver them.

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.