An AI API integration proposal should explain how the feature fits your workflow, what happens when the provider or model cannot respond, and how your team controls access, usage, cost and logs. Compare proposals by asking for specific implementation details and tests—not by accepting broad promises such as “secure” or “scalable.”
Define the workflow and its failure behavior
Describe the task the AI must perform, who starts it, what information it receives, and what your product does with the result. For example, distinguish a draft that a staff member reviews from an answer shown directly to a customer. The difference affects approval steps, error handling and what must be tested.
Ask each supplier to map the path from user action to result, including the application or backend, AI provider, any data retrieval step, and the user interface. Identify which inputs may contain personal, confidential or business-sensitive information, and agree what may be sent to the provider. If the system needs an administrator view or role controls, clarify whether those belong in the integration or in a separate admin panel and backend.
The proposal should also explain what users see when a request times out, is rejected, returns an unusable answer, or cannot be sent because a provider limit has been reached. Possible approaches include a clear retry option, a non-AI fallback, a saved draft, or escalation to a person. The right choice depends on the workflow; require the supplier to specify it rather than assuming the AI will always complete the task.
Specify key handling and access
Request a written account of where provider credentials are stored, which application components can access them, and how they are kept out of public client code, source repositories, and routine logs. Ask how the team will separate development and production credentials, restrict access to the people and systems that need it, and replace a key if it is exposed or access changes.
Clarify ownership as well: whose provider account is used, who controls billing and account access, and what happens to credentials at handover. Do not send live keys in a proposal document or ordinary project messages. Agree on a secure method for providing them during implementation, and ask the supplier to document the process without reproducing secret values.
A credible answer describes the implementation and handover steps—not just a security label. The AI API integration service lists secure key handling among the possible integration scope; confirm how that applies to your proposed model, environment and ownership arrangement.
Request limits, cost controls and useful logs
Ask for controls at both the AI provider and application levels. Provider account limits may help cap or alert on overall usage, while application controls can restrict who may trigger requests, how often, and with what input size. Confirm which controls are actually available for the selected provider and which must be built into your system.
Cost estimates should state their assumptions: expected request volume, typical input and output size, model or service selection, retries, and any other provider charges included. Ask what happens when an agreed usage threshold is approached or reached, who receives alerts, and whether the system pauses, queues, degrades gracefully, or requires an administrator to approve further use. Any estimate should identify the pricing information and date it relies on; verify current provider pricing directly before committing.
Logs help diagnose failures and investigate unexpected usage, but recording full prompts and responses may expose sensitive information. Specify which events and fields are logged, who can view them, how long records are retained, and whether prompt or response content is excluded, masked or stored only when necessary. Ask the supplier to distinguish application logs, provider-side records and any usage or billing dashboard.
| Requirement to put in the brief | What the proposal should specify | How to verify it |
|---|---|---|
| Key storage and access | Storage location, access roles, environment separation, rotation or replacement process, and handover ownership | Review the deployment design and access setup; confirm no key appears in client code, repository history or sample logs |
| Usage and cost limits | Which provider and application controls apply, threshold behavior, alerts, and assumptions behind cost calculations | Walk through a test at the agreed limit; inspect the relevant settings or dashboard and confirm the expected response |
| Logs and data handling | Events and fields recorded, access, retention, and treatment of prompt and response content | Trigger a test request and review a sample log for useful diagnostics and unintended sensitive content |
| Failure behavior | Timeout, provider error, rejected request, unusable output and limit-reached behavior | Run the agreed failure scenarios and confirm the user-facing result and any staff escalation |
| Test and handover materials | Test cases, acceptance evidence, source code and operational documentation included in delivery | Review the test results and handover materials before accepting the work |
Set tests and acceptance criteria before comparing proposals
Turn requirements into observable pass or fail checks. Include a normal request, missing or malformed input, an unavailable provider, a timeout, a provider rejection, an unusable response, a usage-limit event, and an attempt by an unauthorized user. If the system should require a person to review outputs, test that approval step too.
For each case, define the expected user experience, whether the request is retried, what is recorded, and whether any cost or usage counter changes. Ask suppliers to state how they will test the integration, what evidence they will provide, and what is excluded. Avoid criteria such as “AI works well”; define the output format, required fields, handling of uncertainty, and when a human must review the result.
Compare proposals against the same workflow, controls and acceptance criteria. A lower-scope offer may omit monitoring, admin controls, failure paths, or handover documentation, so ask for exclusions and assumptions in writing. Confirm whether delivery includes source code, documentation, testing and launch support, and identify any ongoing provider-account or maintenance tasks your team must own.
FAQ
Should the AI API key be stored in the mobile app or browser?
Ask the supplier to explain the architecture and how credentials are protected from exposure to end users. Do not assume that hiding a key in an app or browser makes it private; have the proposal identify which trusted component makes provider requests and how access is restricted.
Should we log prompts and AI responses?
Not automatically. Logs should support troubleshooting and usage review while matching your data-handling needs. Agree which fields are necessary, whether content is excluded or masked, who can access records, and how long they are kept. Test the resulting logs with representative data before launch.
How can we compare cost estimates fairly?
Give every supplier the same usage assumptions and ask them to show the calculation, selected provider or model, included charges, retry assumptions and pricing reference date. Treat the result as an estimate rather than a guarantee, and confirm current prices and account controls with the provider.
To prepare a project brief, share the AI task and user journey, target app or system, preferred provider if known, expected usage assumptions, data types involved, required limits and alert behavior, logging preferences, test cases, and who will own the provider account. Include relevant existing architecture, access roles and handover needs. Then discuss requirements to define the proposed scope.