Builder Integration Checklist for Client AI Apps

A Builder integration checklist keeps a client AI application from going live with fuzzy ownership, unclear usage units, and billing surprises. For development agencies, it is the pre-launch pass that turns a delivered AI feature into something measurable after handoff.

The important boundary is simple: the client application is built, hosted, and controlled outside ShareAI. ShareAI is the marketplace and API layer that can route AI inference traffic, handle customer-paid usage, apply a Builder margin or surcharge, and support monthly Builder payouts based on generated earnings.

Use this checklist before launch, before pricing conversations get vague, and before support teams inherit an AI workflow they cannot explain.

Builder Integration Checklist: What To Confirm Before Launch

The goal is not to turn every agency project into the same pricing model. The goal is to make the AI traffic traceable, billable, explainable, and aligned with the client outcome.

Area	Question to answer	Launch output
Ownership	Who owns the client app and user relationship?	A clear Builder and client boundary
Usage	What unit best represents AI value?	Tickets, documents, runs, messages, reports, or workflows
Routing	Which AI calls route through ShareAI?	A defined route for production inference traffic
Margin	How will the Builder margin or surcharge be set?	A pricing rule the client understands
Reporting	How will usage be reviewed after launch?	Request labels, client reporting, and support notes

1. Confirm The Client App Boundary

Start by documenting what ShareAI is and is not doing in the client setup. ShareAI is not the app builder, CMS, hosting platform, or workflow builder. The agency or client still owns the application, user experience, data model, permissions, and business logic.

ShareAI fits behind the AI feature. The application sends selected inference traffic through ShareAI, and that traffic can become the basis for usage billing and Builder earnings. That distinction helps the client understand why the integration does not replace the agency’s product work.

• Confirm the Builder: the agency, app owner, maintainer, or product team responsible for the AI traffic.
• Confirm the customer: the user, client, workspace, or end customer paying for routed usage.
• Confirm the app surface: chatbot, portal, CRM workflow, CMS plugin, support automation, commerce feature, or internal tool.
• Confirm the handoff owner: who handles client questions about pricing, usage, support, and feature behavior.

2. Choose Usage Units Your Client Understands

AI costs often start in technical units such as input tokens, output tokens, model calls, and cached context. Those details matter. OpenAI’s API pricing is one example of how model choice and usage type can affect cost.

Clients usually need a business-facing unit. A support leader may understand tickets resolved. A legal operations team may understand documents reviewed. A commerce team may understand product descriptions generated or review summaries created.

Pick a unit that connects AI consumption to client value. Then map that unit back to the underlying ShareAI-routed inference usage.

• Support automation: AI answers, ticket summaries, deflections, or escalations.
• Document workflows: documents processed, sections summarized, entities extracted, or drafts generated.
• CRM automation: leads qualified, notes summarized, follow-ups drafted, or records enriched.
• CMS and commerce: product descriptions, content rewrites, search queries, review summaries, or recommendations.
• Internal tools: department requests, report generations, workspace usage, or employee assistant runs.

3. Map The ShareAI Routing Path

Before launch, decide which production AI calls should route through ShareAI and which should stay outside the monetized path. Not every request needs the same model, margin, or customer-facing treatment.

The technical handoff should identify the user action, the AI request, the model or model class, the fallback expectation, and the usage record needed for reporting. Teams can use the ShareAI documentation and API reference as the implementation starting point.

• Trigger: what user or system action creates the AI request?
• Route: which requests go through ShareAI in production?
• Model choice: which model options fit the feature, latency need, and cost profile?
• Fallback: what should happen if a route is unavailable or too slow?
• Logging: what request ID, tenant ID, client ID, or workspace label should be kept for support?

4. Price The Builder Margin Before Customers Use It

The cleanest pricing conversation happens before the first invoice. A Builder margin should be tied to the value of the client app, not presented as a random markup. If the AI workflow saves time, deflects support tickets, processes documents, or qualifies leads, the pricing logic should be easy to defend.

The money flow should be written down in plain language: the client app routes selected AI inference traffic through ShareAI, the Builder configures a margin or surcharge, the customer pays ShareAI for routed usage, and ShareAI pays the Builder monthly based on generated earnings.

This is recurring usage-based revenue potential, not guaranteed income. If the client does not use the AI feature, there is no usage volume to monetize.

5. Tag Usage For Reporting And Support

Usage tagging is where many client AI launches get messy. A support ticket, chatbot conversation, and background workflow may all call a model, but they should not be impossible to separate later.

At minimum, decide how your app will preserve enough context for operations and client reporting. Keep the labels business-readable, because account managers and client stakeholders may use them after the engineering team has moved on.

• Client or tenant ID.
• Workspace, department, or end-customer label.
• Feature name, such as support summary, lead qualification, or document review.
• Usage unit, such as conversation, run, ticket, document, or workflow.
• Request timestamp and internal request ID.
• Customer-facing status, such as completed, failed, retried, or escalated.

6. Plan Limits, Security, And Failure Handling

A production AI feature needs more than a successful demo. Decide what happens when usage spikes, a user sends unexpected input, a model output needs review, or a downstream workflow fails.

For security planning, the OWASP Top 10 for LLMs and Gen AI Apps is a useful external reference for issues teams should review, including prompt injection and unsafe tool behavior. Do not turn this into unsupported compliance language. Treat it as a practical review step.

• Set usage alerts for unusually high volume.
• Define what happens when the client reaches an included usage level.
• Document fallback behavior for failed or delayed AI requests.
• Decide which outputs require user confirmation before they affect client systems.
• Keep sensitive prompts, logs, and retention expectations aligned with the client’s own policies.

7. Prepare The Client Handoff

The client handoff should make the AI feature understandable to non-engineers. A good handoff explains what the feature does, what usage unit is being tracked, how payment works, what the Builder margin means, and who reviews usage after launch.

This is especially important for agencies. The agency may have built the first version, but the client will live with the feature every day. Clear handoff notes reduce confusion and make the ongoing value easier to defend.

• Feature owner and support contact.
• Usage unit and example billable actions.
• Included usage, paid usage, or top-up policy if applicable.
• Where the client can see usage or request reports.
• Known limits, fallback behavior, and escalation path.
• What changes require a pricing or implementation review.

A Simple Launch Checklist

Before the client AI app goes live, make sure every item below has an owner.

• The client app is clearly owned and operated outside ShareAI.
• The Builder role is documented.
• The AI feature has a business-facing usage unit.
• The ShareAI-routed requests are identified.
• The model, route, and fallback behavior are documented.
• The Builder margin or surcharge is approved.
• The customer payment flow is explained in client-facing language.
• Usage tags are defined for reporting and support.
• Limits, alerts, and failure behavior are defined.
• The client handoff includes pricing, usage, and support notes.

For more implementation-focused articles, browse the Developers category, then open the Builder Console when you are ready to connect app traffic and configure usage margin.

FAQ

What is a Builder integration checklist?

A Builder integration checklist is a pre-launch review for teams routing AI usage from an existing app through ShareAI. It covers ownership, usage units, routing, margin, customer payment, reporting, and handoff.

Is ShareAI used to build the client application?

No. The client application is built and controlled outside ShareAI. ShareAI provides the AI marketplace, API, routing, usage, billing, surcharge, and payout layer for selected inference traffic.

Who should use this checklist?

It is useful for development agencies, AI automation agencies, SaaS teams, plugin developers, chatbot teams, and internal software teams that already own an app with AI usage.

What should be defined before ShareAI routing goes live?

Define the AI feature, usage unit, request route, model choice, fallback behavior, customer payment flow, Builder margin, reporting labels, and support owner before production usage begins.

How should agencies choose usage units?

Agencies should choose units that clients recognize, such as tickets resolved, documents processed, agent runs, support conversations, reports generated, or leads qualified. The unit should connect AI cost to business value.

How does customer payment work for Builder usage?

The app routes selected AI inference traffic through ShareAI. The customer pays ShareAI for the routed usage, and the Builder can earn monthly payouts based on the configured margin or surcharge.

What is the difference between Builder payouts and Provider rewards?

Builder payouts come from AI traffic routed from the Builder’s app and include the configured margin or surcharge. Provider rewards are separate and relate to contributing eligible compute capacity to the ShareAI network.

Should every AI feature route through ShareAI?

Not necessarily. Route the features where usage is valuable, variable, and worth tracking. Some admin-only, test, or non-billable requests may stay outside the monetized path depending on the product design.

How should a client be told about usage-based AI pricing?

Use plain language. Explain the billable action, why heavy usage costs more, what is included if anything, how paid usage works, and how usage reports will be reviewed after launch.

Does this checklist apply to self-hosted or client-controlled deployments?

Yes, when the deployment sends selected AI inference traffic through ShareAI. Be careful with privacy and compliance language: ShareAI can be described as the traffic and billing layer, not as a blanket compliance guarantee.

What should be monitored after launch?

Monitor usage volume, failed requests, unusually heavy users, model choice, customer questions, margin assumptions, and whether the usage unit still reflects the value the client receives.

What is the next step after the checklist is complete?

Open the Builder Console, connect the relevant app traffic, configure the usage margin, and keep the client-facing pricing and support notes aligned with the implemented route.