You don't have an AI hiring problem. You have people who could do this and nobody has taught them how.
Twelve weeks inside your organization, on your platform, against your documents. Your staff leave with two agents they built themselves and the judgment to maintain them without us.
$18,000
12 weeks. Up to 5 people from one organization. Weekly working sessions, not lectures.
$3,000 /mo.
Optional, starts week 13. Ongoing review of what your operators ship. Cancel with 30 days notice.
Every organization we talk to says the same thing: we can't find AI talent locally. The analyst who already knows your data, the specialist who knows your policy, the systems lead who knows your permission model. They are the talent. They have never been taught the material.
Weeks 1 to 5. Retrieval behavior, non-determinism, grounding versus generation, permission inheritance, workflow decomposition. Platform independent, because the platform will change.
Weeks 6 to 9. They build two working agents on your stack, against your documents, inside your security model. We sit beside them.
Weeks 10 to 12. Test protocols, change control, what to do when the model updates and the output shifts. The part nobody teaches.
They keep the standards, the runbook, and the agents. Next quarter's use case does not require another purchase order.
Four artifacts that stay in the building after the twelfth week.
How your files have to be structured for an agent to retrieve them correctly.
The checks an agent has to pass before anyone in the business relies on it.
What happens when the vendor ships an update and the answers move.
Built by your people, not handed to them. That distinction is the whole program.
One organization per cohort. Weeks 6 through 12 run against your real documents inside your real permission model, so cohorts cannot be mixed across companies.
Five load-bearing fundamentals taught through how systems fail, not how vendors demo them. Retrieval, non-determinism, grounding, permissions, decomposition.
Outcome: They can tell you why an answer was wrong.
Configured to your platform. Copilot Studio, Gemini and Agentspace, a custom API stack, or your core vendor's agent builder. They scope, build, and ship two agents.
Outcome: Two agents in production, built in-house.
Regression testing, change control, model update drills, and the handoff of every standard and runbook they wrote along the way.
Outcome: The next use case does not need us.
Five ways to get AI capability into an organization. Four of them leave the knowledge somewhere other than your building.
| Option | Cost | Time to Capability | Where the Knowledge Ends Up |
|---|---|---|---|
| Hire an AI engineer | $180,000+ per year | 6 to 12 months to fill | With one person, who can leave |
| Vendor forward-deployed engineer | Bundled into the contract | Fast | With the vendor, by design |
| Consultancy build | $50,000 to $150,000 | One project | In a deliverable nobody can modify |
| Online courses | $500 per seat | Self-paced | Nowhere. Nothing gets built. |
| AI Operator Program | $18,000 for up to 5 people | 12 weeks | In your staff, with two agents to prove it |
Don't rent the capability. Own it.
Organizations that already bought the platform and now need somebody inside the building who can actually run it.
Up to five people from the same company. Your internal policy structure and permission maps do not go in a shared room with a competitor.
Led by a Syracuse University AI professor who also builds and runs AI operations for clients every week. Teaches it and builds it.
The fundamentals outlive any vendor. Weeks 6 through 12 get configured to whatever stack you have already committed to.
The curriculum is being run inside a partner organization first. If you want your people in an early cohort, book a 30-minute call and we'll walk through the twelve weeks and whether your stack is ready for it.
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