Data handling
Map what AI needs to read, remove unnecessary fields, use representative samples where possible, and document any approved provider or system access.
Trust and procurement
AI workflows should be defensible before they are scaled.
Verdify helps operational teams design Verified AI systems with narrow access, explicit action limits, human approval, system-of-record authority, telemetry, and scorecards. This page gives buyers, security reviewers, and procurement teams the starting posture.
Operating controls
The trust model is built into the workflow, not appended later.
Verdify's delivery artifacts make security, data, and governance review concrete enough for executives and technical owners to inspect.
Access control
Use least-privilege access, named owners, approval paths, and clear separation between read, draft, recommend, and execute capabilities.
Model and provider choices
Keep architecture provider-neutral until client requirements, data sensitivity, retention needs, and procurement constraints are known.
Telemetry and logs
Define the events that must be logged: inputs, recommendations, approvals, overrides, exceptions, system-of-record updates, and scorecard outcomes.
Regulated-data posture
Do not move regulated, confidential, or customer-sensitive data into model workflows without explicit review, documented controls, and client approval.
Incident review
Create an exception taxonomy and review cadence so bad recommendations, missing evidence, drift, and approval failures become operating signals.
What Verdify will not do
Procurement risk often comes from unclear promises.
Verdify's default answer is restraint until the workflow has an evidence plan and accountable owner. That makes some AI ideas slower to launch, but easier to defend.
We do not ask AI to take irreversible, regulated, unsafe, or customer-facing actions without explicit controls.
We do not replace systems of record with model output.
We do not ingest sensitive client data into third-party tools unless the client has approved the path.
We do not make impact, safety, or autonomy claims without scorecard evidence.
We do not hide known limits, failed test cases, missing telemetry, or unresolved approval gaps.
Review artifacts
A trust review should have evidence.
These are the kinds of deliverables Verdify uses to make an AI workflow reviewable before implementation expands.
| Artifact | What it answers | Why procurement cares |
|---|---|---|
| Control Matrix | What AI may read, draft, recommend, execute, and never touch. | Shows where authority is constrained before production use. |
| Risk register | What can go wrong, who owns it, and what evidence would trigger review. | Turns vague AI risk into accountable operating issues. |
| System-of-record map | Which systems remain authoritative for facts, approvals, and final writes. | Prevents model output from becoming an unmanaged record. |
| Telemetry plan | Which events, overrides, exceptions, and outcomes are logged. | Supports auditability, incident review, and vendor oversight. |
| Scorecard | Which metrics prove whether the workflow improved. | Keeps expansion tied to evidence instead of enthusiasm. |
| Trust posture review | Which security, legal, data, access, provider, and claim-limit questions must be answered. | Gives buyers a clear review path before sensitive data, new providers, or expanded authority are introduced. |
Proof pattern
Verdify applies the same discipline to its public lab.
The Live Lab makes the operating pattern inspectable: the AI agent plans, control layers constrain writes, firmware controls, telemetry verifies, and scorecards and lessons close the loop.
Live Lab
The public greenhouse proof layer and its claim limits on lab.verdify.ai.
Greenhouse case study
A narrative proof artifact with operational evidence, caveats, and the business translation.
Control Loop Blueprint
The flagship resource for mapping a measured feedback loop.
Control Matrix
A practical resource for defining what AI may and may not do.
When this trust posture is useful.
Good fit when
You have a real workflow where wrong AI actions would matter.
Security, quality, procurement, or legal reviewers need clear controls before implementation.
The team can name systems of record, approval owners, and evidence sources.
You want a scorecard before expanding AI authority.
Not a fit when
You want unchecked autonomous actions without approval or logging.
You need Verdify to ingest sensitive data before scope and controls are agreed.
You want generic AI strategy theater without operational artifacts.
You will not preserve human or system authority where consequences matter.
FAQ
Common buyer questions.
Does Verdify need production access to start an audit?
No. A Verified AI Operations Audit can begin with interviews, workflow samples, screenshots, exported records, policy documents, and representative data. Production access is only considered when the workflow, risk, scope, and access controls justify it.
How does Verdify handle regulated or sensitive data?
The default is data minimization: use representative samples, redact unnecessary fields, preserve source traceability, and avoid moving regulated or sensitive data into model providers unless the client has approved that architecture.
Can Verdify work inside our existing model, cloud, or governance stack?
Yes. Verdify's method is provider-neutral. The core work is defining action limits, approval paths, systems of record, telemetry, scorecards, and operating cadence, then fitting implementation choices to the client's constraints.