Audited AI apps are applications where execution steps, model calls, outputs, usage and proof signals are structured from the beginning. This makes the app more suitable for serious clients and regulated environments.
Operational information, not legal advice.
Developer execution path
Builder entry
Start from SDK, API or reference app entry points instead of uncontrolled prompt work.
Execution path
Structure intake, execution, output and review as one governed workflow.
Proof layer
Keep run context, artifacts, usage and audit signals connected to the execution record.
Developer handoff
Move the pattern into an app, workflow or API-backed implementation.
Developer execution model
Builder entry, execution path, proof layer and developer handoff stay visible as one repeatable implementation model.
Platform proof
An audited AI app is not only a user interface around a model. It needs defined inputs, controlled steps, visible outputs, usage accounting and proof-oriented metadata so that serious clients can inspect how work was produced.
Follow the builder path through the M13 API, inspect the AI execution ledger, or start building audited AI apps.
Execution structure
Builder path
This page describes platform and developer architecture for audited AI execution. It is not legal advice.
Related guides
Understand how auditable AI workflows connect steps, state, outputs and proof.
Use M13 operations to build AI apps with structured responses, usage, billing and execution context.
Use the EU AI Act Diagnostic as a learning path for building audited AI applications on m-pathy.
See how the EU AI Act Diagnostic works as a reference app for audited AI execution on m-pathy.
Official sources
Google documentation explaining how structured data helps search engines understand page content.
Official European Commission overview of the EU AI Act and its risk-based regulatory framework.
External references are included for source orientation. This page remains operational information and is not legal advice.