Three ways in. All of them assume the same starting point: that the work has to be described before it can be mechanized, measured, or improved.
One critical workflow. One operating decision. A bounded evaluation of how the work performs.
The buyer is the person who owns the decision: a chief operating officer or business-unit leader with a pilot waiting to scale, a transformation team that has to defend a rollout, or a portfolio operating partner deciding what is worth repeating.
A review runs under the confidentiality terms agreed with the customer. Using the work in research, or as a published case, requires separate permission. The review supports an operating decision; it is not a certification and it does not issue a regulatory approval.
We organize a body of your work before you mechanize it. The output is a set of Work Composition descriptions covering real tasks, an account of where judgment sits, and a measurement scheme for the parts you intend to delegate.
Every engagement is instrumented. You keep the descriptions; the lab retains the right to publish findings in aggregate and anonymized form. That condition is not negotiable, because it is what makes this research rather than consulting.
For organizations, funders, and bodies with a measurement question that does not yet have an answer. Scope is agreed in advance and results are published. We do not undertake work whose findings are contingent on the sponsor's preference.
The agentic AI supervisor course runs directly for corporate cohorts. Participants work on their own tasks and leave with completed canvases. An open cohort for individuals is not yet scheduled; write to the lab to be told when one is. A separate open course by the same instructor, Knowledge Manufacturing for Learning and Development, runs on Maven now.
Write with the question you are trying to answer. A paragraph is enough to tell whether this is a fit.
