Claim
What the AI workflow is supposed to improve: speed, quality, traceability, exception handling, or supervision.
Proof
Proof should be operational, not performative.
Verdify uses evidence, scorecards, explicit limits, and the public Live Lab to show how Verified AI operations work before asking buyers to take the claim on trust.
Greenhouse Case Study
A physical proof layer with telemetry, controlled action, public scorecards, and honest limits.
Control Loop Blueprint
The primary downloadable resource for translating the lab pattern into a business workflow.
Control Matrix
Define what AI may read, draft, recommend, execute, and never touch.
Live Lab
The external source of truth for public operations, plans, scorecards, safety notes, and caveats.
Proof standard
Every proof artifact should name the claim, controls, evidence, and caveat.
The point is not to make AI look impressive. The point is to make the workflow inspectable enough that a buyer can decide whether the evidence supports the next step.
Controls
What AI may read, draft, recommend, execute, and what it must never touch.
Evidence
Telemetry, logs, source traces, reviewer decisions, scorecard metrics, and before/after comparisons.
Caveat
What the proof does not show yet, including confounders, known limits, and unresolved risks.
Proof pages are useful when buyers need evidence before a call.
Good fit when
You need to understand Verdify's operating posture before involving a team.
Your workflow has actions that need explicit controls.
You want examples of artifacts before starting an audit.
You value caveats and known limits as part of the proof.
Not a fit when
You want broad AI transformation claims without operating details.
You expect public client data before a client has approved publication.
You want a greenhouse automation product.
You do not want to define prohibited actions or scorecard metrics.
FAQ
Common buyer questions.
What counts as proof before there are public client case studies?
Verdify uses artifact proof: control matrices, scorecards, explicit excluded actions, and the public greenhouse lab. Client case studies will use the same structure when they are publishable.
Why include what was not automated?
For high-trust AI workflows, restraint is evidence. Naming what AI was not allowed to do shows the action limits, approval model, and operating discipline.
Is the greenhouse proof causal proof?
No. The greenhouse comparison is operational evidence with caveats, not a controlled A/B test. Its strongest proof is auditability: planner availability, telemetry, stress, cost, and score are visible enough to inspect.