Expertise and workflow inventory
Ranked list of candidate workflows with owner, expert judgment, volume, source systems, current pain, risk level, and measurement path.
AI Learning Loop Audit
Know what AI capability the organization should own before you build.
A fixed-scope audit that maps where human expertise, source knowledge, workflow traces, AI model behavior, feedback, and outcomes should compound into organization-owned AI advantage.
Evidence from the lab
Evidence from the lab: the audit starts with the loop.
In Verdify Lab, the first design question was not whether AI could control the greenhouse. It was how people, source context, AI proposals, control layers, telemetry, and outcomes would create an inspectable learning loop.
Inspect the Greenhouse Case StudyWhat transfers to an audit
Map expertise, source knowledge, workflow traces, feedback, and outcomes before choosing tools, prompts, or AI model providers.
Define allowed, conditional, and prohibited AI actions before implementation.
Name the authority layer, telemetry events, private evals, known limits, and decision gate before any build.
Deliverables
The audit ends with learning-loop decisions, not a vague recommendation deck.
Each artifact is meant to help the team decide which workflow to build first, what expertise and knowledge the organization should own, and what evidence must exist before AI authority expands.
AI capital map
Where the organization can turn knowledge, AI model output, review decisions, corrections, and outcomes into reusable capability.
Control Matrix
Allowed, conditional, and prohibited AI actions, including customer-facing, regulated, irreversible, and system-of-record writes.
Risk register
Failure modes, owners, mitigations, escalation triggers, required evidence, and unresolved procurement or data issues.
Private eval and telemetry plan
Event list for inputs, recommendations, approvals, overrides, exceptions, handoffs, outcome metrics, and review cadence.
Executive readout
Recommended first workflow, scope, investment logic, non-goals, and the decision needed to start or defer the first learning loop.
Scope
What the audit requires from your team.
The audit is fixed-scope, but it is not passive. Verdify needs enough context to map expertise, evidence, feedback, and workflow reality.
Typical timeline
Two to four weeks, depending on stakeholder count, system complexity, and how quickly workflow samples can be reviewed.
Client inputs
Workflow walkthroughs, sample tickets or documents, current process notes or policies, system screenshots, reporting exports, risk concerns, and decision-owner interviews.
Decision produced
Build, defer, or reject the first AI learning workflow, with the controls, private eval, owner, and implementation path explicit.
Sample artifact
Example audit output: medtech design-change evidence pack.
This is the level of specificity the audit is designed to produce before anyone builds. The same structure can be applied outside medtech when expertise, evidence, feedback, and approval paths are explicit.
| Workflow step | AI may | AI may not | Evidence |
|---|---|---|---|
| Change intake | Classify change type and affected records. | Approve the change or close CAPA. | Change request ID, source docs, reviewer decision. |
| Traceability review | Map requirements to tests and flag missing links. | Invent source evidence or alter controlled documents. | QMS record IDs, document revisions, trace completeness. |
| QA/RA review prep | Draft a review packet and unresolved-gap memo. | Release the device or submit regulatory changes. | Approver IDs, exception log, approval-ready evidence packet. |
Support escalation remains a valid simpler audit pattern. This example shows how the same audit structure handles document-heavy workflows where reviewer-approved evidence matters.
The audit is the right first step when the learning loop is unclear.
Good fit when
You have AI ideas but no clear priority.
A workflow has enough volume or consequence to justify controls.
Leadership wants evidence before expanding AI use.
You need a practical first implementation path.
Not a fit when
You want generic AI training without a specific workflow.
You want unchecked AI actions without capturing human judgment or outcome evidence.
You cannot provide stakeholder access for workflow discovery.
You need a large platform rebuild before workflow controls can be discussed.
FAQ
Common buyer questions.
Is this a strategy workshop or an implementation plan?
The audit produces an implementation-ready roadmap. It includes an expertise map, knowledge architecture review, workflow inventory, action limits, systems-of-record review, private eval plan, telemetry plan, and a recommended first learning-loop build path.
Do we need an AI prototype already?
No. The audit is useful before a prototype exists, especially when the team has multiple AI ideas but no shared definition of what expertise, knowledge, feedback, and outcomes the organization needs to own.
What makes this different from AI readiness work?
The audit is workflow-specific and advantage-oriented. It focuses on organization-owned knowledge, expert judgment, private evals, feedback loops, allowed and prohibited actions, and the first implementation decision.