The Training Wing · For mid-market leaders in non-tech industries

Most AI training teaches tools.
We teach systems thinking.

We make sure your data, your processes, and your people are AI-ready — and stay that way as the tools evolve. Not with hype. Not with another chatbot demo. With the same disciplined approach that turned battlefield chaos into institutional capability.

The Backstory

This isn’t my first time walking into technological chaos.

In the 1990s, the Marines were drowning in new technology nobody asked for. Information systems from every service — none built for us, none talking to each other — were flooding our commanders with data that made them slower, not faster.

Five of us decided to fix it. We didn’t start with the technology. We started with our doctrine — our proven processes written in blood — and one question: What does the commander actually need to make a decision?

We built the discipline from scratch. I became the Marine Corps’ first Information Management Officer and helped shape a specialty that exists throughout the entire Department of Defense to this day.

That’s the same question I bring to businesses today. Not “what can AI do?” but “what does your business actually need to succeed — and how does AI serve that?”
The Real Cost of Confusion

Companies without a strategy don’t save money.
They just spread the waste across more categories.

$30K–$80K

Wasted on overlapping tools

Multiple teams buying different AI subscriptions. No coordination. Maybe 20–30% produces lasting value. The rest quietly dies on someone’s credit card statement.

$50K–$150K

Lost to unproductive experimentation

When “experiment with AI” is the strategy, you get hundreds of hours of testing, demoing, debating, and building workflows that get scrapped. Loaded labor cost with nothing to show.

Immeasurable

Organizational fatigue

After six months of scattered effort, the team is more skeptical, the budget is tighter, and you’re starting from roughly the same place — except now the next initiative is harder to launch.

The primary cost isn’t the money burned. It’s the time-to-capability delay and the organizational fatigue that makes the eventual real initiative harder to start.

The Methodology

Four tiers of AI capability.
Most companies stop at one.

Everyone learned prompt writing. It’s table stakes now. The companies that pull ahead are mastering the layers above it — and almost nobody is teaching them.

1

Prompt Writing

Table stakes

Structuring clear instructions with examples and guardrails. This is where everyone starts. It works. It’s also where most training ends — and that’s the problem.

2

Context Engineering

The overlooked 99.98%

Curating the full information environment an agent operates in. System prompts, tool definitions, retrieved documents, memory. Your prompt is 0.02% of what the model sees. The rest is context.

3

Intent Engineering

Where outcomes are decided

Encoding your goals, your trade-off hierarchies, your definition of “good” into the system. Without this, agents optimize for the wrong thing — fast but wrong, efficient but off-mission.

4

Specification Engineering

The 10x gap

Writing documents so complete an autonomous agent executes against them for hours without checking in. SOPs, decision rules, role definitions — tagged, queryable, version-controlled. This is where the real leverage lives.

Hands-On Tracks

Tool fluency, taught the systems way.

Focused, practical cohorts for teams that need working capability in a specific part of the stack — each one taught inside the four-tier framework, so the skills compound instead of expiring with the next release.

T1

Claude Cowork for Teams

Deploy Claude at the organizational level. Teams learn to build workflows, prompts, and integrations inside Anthropic’s collaboration environment. Moves from individual use to coordinated, systematic AI operation. Ideal for teams of 3–50 getting started with structured AI adoption.

T2

Claude Code & IDE Mastery

Hands-on training in Claude Code, Cursor, and Antigravity for developers and technical leads who need to build and maintain AI-integrated codebases. From setup to production workflow in a single cohort.

T3

n8n Workflow Engineering

From fundamentals to production: build, deploy, and maintain automation workflows in n8n. Covers API integrations, error handling, and real-world business logic. Hands-on. No prior automation experience required.

T4

Custom Cohort Programs

Bespoke training programs built around your team’s specific tools, processes, and operational maturity. We audit first, then design the curriculum around what will actually move your numbers. For organizations that need more than a course.

The Thesis

AI readiness isn’t a project.
It’s a permanent posture.

The companies that win the next five years won’t be the ones that deployed AI first. They’ll be the ones whose data, processes, and people stayed ready as the tools evolved. We’re not here to sell you a tool or tell you AI will solve everything. We’re here to make sure that whatever ships next — from OpenAI, Anthropic, Google, or someone who doesn’t exist yet — you’re already prepared.

Your data stays AI-ready

Structured, tagged, version-controlled, and accessible by any agent on any platform. Not locked into one vendor’s ecosystem.

Your team stays current

Not generic AI training — specifically how to use what just shipped against your specific business processes and knowledge.

Your specs stay fresh

When capabilities change, your agent instructions update. Automated drift detection catches what humans miss. Quarterly audits catch what automation misses.

Next Step

See the programs.
Then make the call.

Four programs, one trajectory — from strategic literacy to a knowledge stack any AI agent can execute against. Formats and investment are on the programs page.

Programs & Pricing Book a Strategy Call

Available in English and Spanish · Remote and on-site engagements