1. Capture context
Identify the work, source, requester, current state and required outcome.
Governed execution separates interpretation, policy, authority and action so each can be tested and improved independently.
Map this opportunity →When model reasoning and system action are fused together, operations teams cannot see where an error occurred, who had authority or how to recover safely.
Each layer has a clear purpose, boundary and decision owner.
Identify the work, source, requester, current state and required outcome.
Use the model to structure ambiguity; preserve the original evidence and confidence.
Run deterministic validations and decide whether the work may proceed.
Pause, approve, reject, amend or recover through an accountable human decision.
Rules decide whether work can proceed, pause or enter review.
Material ambiguity and high-risk actions enter a controlled state.
Decision context, action and outcome remain visible.
The commercial path is staged so a buyer can test workflow fit, controls and measurable value before expanding scope.
Name one workflow, operating pressure, safe evidence source and required business outcome.
Establish the baseline, exception pattern, authority model, system boundary and whether a pilot has a defensible case.
Test one agreed path with explicit acceptance measures, exception ownership and controlled permissions.
Extend channels, systems or decision rights only after the operating evidence supports the next boundary.
Governed execution separates AI interpretation from deterministic policy, human authority and system action. The incoming evidence, model output, validation result, approval, action and outcome remain linked so the workflow can be reviewed and recovered.
An assistant normally returns an answer. A general-purpose agent may plan and act across broad tools. GZP starts with one bounded operational workflow, explicit business rules, limited action permissions, named exception owners and measurable acceptance criteria.
The assessment examines workflow volume, cycle time, manual touches, recurring exceptions, evidence availability, decision authority, system boundaries and the outcome the operating team can verify.
GZP does not publish a universal pilot duration or price before the workflow is understood. Scope depends on channels, transaction volume, exception complexity, evidence quality, integration boundaries and agreed acceptance measures.
Yes. A read-first or decision-support scope can establish whether an exception pattern is repeatable and material before any remediation or system-write permission is introduced.
No. Material ambiguity, high-value actions and policy exceptions can be routed to an accountable person. The objective is to make authority explicit and efficient, not to remove it by default.
Every material action needs a visible decision path: what the model understood, what policy allowed, who approved, what system changed and whether the intended outcome was achieved.