Your developers have AI. Your organization doesn't. AI coding tools made individual developers faster. They didn't give your organization a shared way to build software with AI. The result is inconsistent practices, architectural drift, lost context, and growing dependence on senior engineers. The Intelligent Engineering Workshop changes that using the tools you already have.
12 private working sessions · 6 weeks · Up to 10 participants · Your existing stack
Your organization adopted AI tools. It did not adopt an operating model. That gap is why the bottlenecks you expected AI to remove are still there. The problem is more organizational than technical.
Every developer uses AI differently. There is no shared practice, so quality and approach swing widely from one engineer to the next.
AI generates code that doesn't know your architecture or conventions. Left unchecked, every session pulls the codebase a little further from the design your team agreed on.
Decisions, standards, and hard-won context reset every session. The organization relearns the same things over and over, and nothing accumulates.
Your most experienced engineers become the only ones who can correct and direct AI output. The dependency you hoped to reduce grows instead.
AI coding tools changed how developers write code. Intelligent Engineering changes how engineering organizations build software. It is the engineering operating model that turns individual AI use into organizational capability: shared practices, organizational memory, architectural knowledge, and coordinated execution that builds over time instead of resetting every session.
The Workshop installs an operating model, not a list of tips. Once it is in place, your engineering organization has:
A common, repeatable way of working with AI that every engineer follows, so quality no longer depends on who happens to be at the keyboard.
Repeated work becomes shared, triggerable workflows the whole organization can rely on instead of improvising from scratch each time.
Decisions and context become organizational memory that survives sessions, handoffs, and turnover instead of living in one person's head.
Your team establishes repeatable ways to give AI the architecture, standards, and constraints it needs, reducing drift between developers and sessions.
Work runs in parallel with visibility and control, so capacity is directed deliberately instead of one prompt at a time.
Less time spent fixing output that doesn't fit, because the operating model puts the right context in front of the work from the start.
12 working sessions. Six weeks. Your existing stack. Your real work.
Two 2-hour sessions each week, with real engineering work in between.
The time between sessions is intentional. Your team applies each part of the operating model to real work, brings what they learn back into the next session, and builds new practices over time rather than trying to absorb a new way of engineering in a one-week training event.
We begin by mapping your current engineering practices, architecture, AI usage, and workflows to the Intelligent Engineering operating model.
Twice each week, we work together for two hours to introduce and install the Intelligent Engineering operating model inside your team.
Your team puts what we've worked through to use on actual engineering problems. This is where concepts become habits.
Your organization keeps the practices, workflows, and organizational memory it built. The operating model outlasts the engagement and spreads to the rest of engineering.
Could we teach the concepts in a week? Yes. But the goal isn't to teach the concepts. It's to change how your engineering organization operates.
This is not a course, and it is not tips for using AI tools. Training is how we deliver it, but a changed organization is the product.
We install an operating model inside your organization. You leave with capability in place, not notes to act on later.
Your team applies Intelligent Engineering using its existing tools, architecture, workflows, and constraints. Nothing is taught in a vacuum.
Because knowledge sticks and your team shares its practices, the value keeps building after we leave. Organizational memory grows instead of resetting.
Engineering leaders and CTOs who have rolled out AI tools but aren't seeing the organizational change they expected.
Engineering organizations, especially .NET shops, that want a durable operating model rather than another tool or another course.
Not for teams looking for a quick tips session or for someone to write their code for them. This builds organizational capability.
Intelligent Engineering wasn't developed as an AI training framework.
Chad Carter developed it from 29 years of architecting and building production software, including systems used by Fortune 500 companies, a decade as a Microsoft MVP, and three technical books. In the Workshop, that experience is applied directly to your team's engineering practices and workflows.
A six-week hands-on engagement for up to 10 participants.
$21,000
Begin the engagement.
Due before Week 3 once you choose to continue.
$10,500
Get the complete six-week Workshop for half the standard investment.
At the end of Week 2, if you and your engineering lead don't believe the Workshop is delivering clear, practical value, stop. There is no obligation to continue.
Work directly with Chad Carter to put Intelligent Engineering into practice.
12 private, 2-hour working sessions over six weeks.
Delivered remotely using your tools, architecture, and workflows.
Your team keeps the practices and workflows it builds during the engagement.
See if the Workshop is right for your organization with a free Intelligent Engineering Assessment. There's no obligation.
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