Work With Me
Bring me a problem large enough to change the business.
I work with people and companies that have real stakes — not side experiments, not tool shopping, and not AI theater.
When I implement AI inside a company, the work should be able to add six figures or more to the bottom line. That is the bar for direct engagement.
Sometimes the problem is strategic. Leadership knows AI will change how the company operates, but cannot yet separate useful change from expensive distraction.
Sometimes the problem is operational or technical. The opportunity is clear enough to matter, but the system still needs architecture, verification, and judgment before the business can depend on it.
You will need to qualify. Tell me the problem, the stakes, and what a successful outcome is worth. If it is a fit, we will decide how to work.
Most people should not hire me first.
A large part of my work is published freely because access to useful AI knowledge should not depend on whether someone can afford private help.
Builders can start with the Agentic Engineer course, The Adam Repo, and the public experiments behind the systems I am building.
Business leaders can start with the AI for Business Leaders course and the frameworks I publish around agentic companies, judgment, and responsible adoption.
Use that work first.
Direct involvement is for companies and operators where the problem is large enough that a serious AI implementation can move the bottom line by six figures or more — and where the cost of guessing is higher than the cost of getting it right.
Qualification is intentional.
I do not take every request. Capacity is limited, and the work only makes sense when the stakes are real.
A good fit usually looks like this: a company or operator with a concrete problem, enough context to describe it honestly, authority to act on the result, and an outcome large enough that six-figure value is a realistic possibility — through revenue, cost, speed, risk reduction, or a clearer operating model.
If the problem is smaller, earlier, or better served by the free courses and communities, I will tell you that. That is still a useful outcome.
For business leaders
AI strategy is often reduced to a list of tools, pilot projects, and productivity claims. That is rarely the real decision.
The deeper question is how intelligence should move through the company. Leaders need to decide which work can be delegated, where human judgment must remain visible, what evidence is required before an agent acts, and how the organization will learn from the result.
I help leadership teams examine those decisions without pretending every workflow should become autonomous.
The work may include clarifying where AI belongs, mapping one important operating loop, identifying the information an agent would need, and defining the human authority required at each consequential step.
The goal is not to leave with a larger AI roadmap. It is to leave with a decision the team understands and a first move the company can evaluate honestly.
The first useful question is usually smaller than “What is our AI strategy?”
A company does not experience AI strategy as a presentation. It experiences it inside a workflow.
A customer asks for help. Information enters the company. Someone interprets it, makes a decision, performs the work, checks the result, and decides what should happen next.
That is where we begin. We look at one process closely enough to understand what currently requires judgment, where information gets lost, which delays are structural, and whether AI can close the loop without hiding responsibility.
One well-understood workflow can teach a leadership team more than a year of disconnected experiments.
For builders
AI has made it easier to produce working software. It has not made product judgment, system design, or verification optional.
A coding agent may create the feature you described while misunderstanding the problem you needed to solve. A prototype may work during a demonstration and collapse when another person brings unexpected data, unclear instructions, or a different idea of success.
I work with builders who need to move from possibility to something another person can actually use.
That may involve improving the architecture around coding agents, defining clearer acceptance criteria, designing a better verification loop, or working through a product decision that the code itself cannot answer.
The agent can carry more of the implementation. The builder remains responsible for deciding what deserves to be built and proving that the result is ready.
A prototype is evidence, not completion.
Fast generation changes the beginning of the work. It does not remove the middle.
The system still needs to survive incomplete requirements, edge cases, security decisions, changing context, and the behavior of people who did not help build it.
That is where a great deal of agentic engineering now lives.
The work is no longer only about writing the code. It is about creating the environment in which agents can plan, build, test, review, and improve without confusing confident output for verified progress.
When we work together, the focus stays on the system around the agent rather than the novelty of the agent itself.
The work has to produce something inspectable.
A useful engagement should create more than a good conversation.
The result may be a decision framework, a mapped workflow, an architectural direction, a working prototype, a verification plan, or a clear reason not to continue.
The exact artifact depends on the problem. The standard does not.
You should be able to explain what changed, why the new direction is stronger, what remains uncertain, and what evidence should determine the next move.
I do not want you leaving with language that sounds impressive but cannot survive a serious question from your team, customer, or future self.
I will tell you when AI is the wrong answer.
AI can reduce effort, make more context usable, and allow a smaller group of people to attempt work that once required a much larger organization.
It can also add cost, uncertainty, and a new layer of failure to a process that was already working.
The point is not to force AI into the problem. The point is to make the best decision possible.
That may mean building an agentic workflow. It may mean using a model for one narrow part of the process. It may mean improving the underlying information before any model is introduced.
Sometimes the right answer is to leave the process alone. That is still a useful decision.
Responsibility
Human judgment remains part of the architecture.
I do not believe a responsible AI system is one where the human simply approves whatever the machine prepared.
The person needs enough visibility to understand what happened, which evidence influenced the result, and where uncertainty remains.
We will define that responsibility directly.
Who decides what success means?
Which actions can the system take without approval?
Which claims require evidence?
When should the system stop and ask for help?
Who remains responsible when the result affects another person?
Those questions are part of the product and operating model. They should not be left for the team to discover after deployment.
The right engagement begins with a real problem.
The strongest starting point is not “We want to use AI.” It is a situation you already understand well enough to describe honestly — and large enough that solving it changes something material in the business.
Tell me what is happening now, why it matters financially or operationally, what has already been tried, and what a successful outcome would be worth.
You do not need a complete specification. You do need a problem that exists outside the excitement of the technology.
Application
Apply with the problem and the stakes.
Bring the actual problem, including the parts that are unclear, inconvenient, or resistant to the story you hoped would be true.
Include enough context for me to judge whether the work can realistically create six-figure value — and whether I am the right person to help.
If it looks like a fit, I will follow up. If it does not, I will point you toward the free path when that is the better starting point.
Prefer the free path first? Start Building · Start Leading · Community waitlists
FAQ
Frequently asked questions
Do I need to understand AI before reaching out?
No. You need to understand the problem, the people affected by it, and what a useful result would change for the business.
What does “qualify” mean?
Submitting this form is an application, not a booking. I review whether the problem is large enough, whether six-figure impact is realistic, and whether direct work is the right next step.
What if my problem is smaller than that?
Start with the free courses, public systems, and community waitlists. Direct work is reserved for problems with serious stakes.