AI Add-on Studio: company-specific features, reviewed before deployment.

Create

Describe the workflow. Generate the package.

AI Add-on Studio starts with the operating problem: who uses the workflow, what UI it needs, which approvals and controls matter, what data it reads or writes, and what success looks like. AI can generate a company-scoped code package covering pages, fields, Smart Views, reports, workflow rules, dashboard widgets, automations, import mappings, integration adapters, AI templates, tests, sandbox steps, and rollback plans.

• Generated files, tests, permissions, data access notes, and security notes.
• Custom fields, new pages, and module-specific configuration.
• Smart Views, reports, dashboards, widgets, and automations.
• Workflow steps, approvals, routing, and validation rules.
• Import mappings, formulas, integrations, and AI templates for repeatable work.

Sandbox

Test against copied company data before review.

Add-on Studio is designed around copied-data sandbox validation. Teams test the generated package in the sandbox, record the test result, and attach screenshots, files, specs, acceptance notes, and evidence so Quoining can review the exact tested build before anything touches the company UI.

• Requires a copied-data sandbox before submission.
• Users can revert sandbox experiments before submitting.
• Evidence attachments stay linked to the feature submission.
• Submissions are blocked until package, sandbox-test, and evidence requirements are complete.
• AI-generated code remains untrusted until reviewed and approved.

Review

Quoining approves before company-scoped deployment.

Customer users do not deploy directly to production. A submission enters Quoining admin review, where admins can approve, request changes, reject, deploy to only that company instance, or revoke a deployed package. The result is customization without silently changing the product for every customer.

• Admin review queue in admin.quoining.com.
• Request-changes loop back to the customer workspace.
• Company-scoped deployment rather than global rollout.
• Deployment and revoke states are tracked for auditability.

Controls

Permissioned, plan-gated, and evidence-backed.

Access is controlled in Settings > Team with the AI Add-on Studio permission, and the feature is Growth and above. Generation also respects normal AI permissions and AI caps. The chat history, generated code package, evidence, review status, and deployment state stay tied to the company.

• Growth+ entitlement: AI Add-on Studio.
• Per-user access via the AI Add-on Studio permission in Settings > Team.
• AI generation also requires AI permission and plan caps.
• Submissions preserve chat history, evidence, status, reviewer notes, and deployment state.

Why Quoining

Customization without turning the product into a fork.

Finance teams often need one company-specific page, field, approval path, report, automation, or operational workflow that generic software will never prioritize. Add-on Studio gives those teams a governed way to build and test custom behavior, while Quoining keeps the approval and deployment boundary explicit.

• Power users can build iteratively with AI instead of sending a ticket full of guesswork.
• Sandbox evidence reduces ambiguity before admin review.
• Deployments can be company-specific, so one customer's workflow does not become everyone else's UI.