To add an AI chatbot to an iOS or Android app, build the chat interface in the app and send messages to a backend you control. Have that backend authenticate the user, validate and limit requests, keep the AI provider’s secret key off the device, call the provider’s API, and return the response. Before release, test failures and abuse cases, document what data moves through the system, and make your Apple App Store or Google Play disclosures match the app’s actual behavior.
1. Decide what the chatbot is allowed to do
Write down the chatbot’s purpose before building the interface or connecting a model. A support assistant that answers questions from help content has a different risk profile from an assistant that can look up account details or take actions for a user.
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- Define the questions it should answer and the subjects or requests it should decline.
- Decide when it should hand the conversation to a person or another support channel.
- List the app data the chatbot needs, and exclude information that is not necessary for its job.
- If users can ask it to perform actions, specify which actions are permitted and what confirmation the app requires before carrying them out.
These boundaries inform the backend’s access controls, the app’s disclosures, and the failure behavior you need to test.
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The mobile app should call a service you control; that service should call the AI provider. Do not put a long-lived provider API key in iOS or Android code, app assets, or a value that can be extracted from the installed app. OpenAI’s API key safety guidance says requests should be routed through your backend so the key stays secure. The API overview also describes server-side key handling, including loading keys from an environment variable or key-management service.
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| Component | Responsibilities |
|---|---|
| iOS or Android app | Collects the user’s message, displays the conversation and request status, and sends an authenticated request to your service. It does not contain the provider secret. |
| Your backend | Authenticates the user, validates and limits requests, holds the provider credential, calls the AI API, handles provider responses and errors, and returns an appropriate result to the app. |
| AI provider | Receives the backend’s API request and returns a response under the provider’s current API contract and data controls. |
An exposed key can be taken and used to make requests on the developer’s behalf, potentially creating unexpected charges or putting account data at risk. A backend gives you a place to enforce access and usage limits; it does not by itself make the feature safe. Apply appropriate authentication and validation there, too.
3. Build the mobile chat experience
Create a screen that makes it clear when a message is being sent, when a response is ready, and what the user can do if the request fails. The exact implementation depends on your app’s iOS or Android architecture and the provider’s API.
- Provide message entry and a readable display for user and assistant messages.
- Show a loading or in-progress state, and prevent accidental repeated submissions where appropriate.
- Give users a useful failure state and a deliberate way to retry.
- Let users start a new conversation or clear the current one if that is part of your product’s data behavior.
- Decide whether responses need to stream. Confirm support, event handling, and current behavior in the selected provider’s API reference before implementing streaming.
Keep the user-facing experience consistent with what the service actually does. For example, do not imply that clearing a visible conversation also deletes server-side records unless it really does.
4. Implement the request flow against the current API
Use the selected provider’s current API reference for endpoint names, request and response schemas, authentication, streaming events, errors, rate limits, and request IDs. These details can change, so confirm the contract against the live documentation before shipping rather than relying on an old example.
- The app sends a request to your backend. Include the user’s authenticated session and only the message or app context needed for the chatbot’s task.
- The backend checks the request. Authenticate the user, validate the input, and apply per-user and global limits before contacting the provider.
- The backend calls the AI API. Retrieve the provider credential from server-side configuration or a key-management service; do not ask the mobile client to supply it.
- The backend handles the result. Account for successful responses as well as provider errors, rate limits, timeouts, and responses the app cannot use.
- The app updates the conversation. Display the returned response or a clear failure state, and offer retry behavior that does not silently submit the same request repeatedly.
Choose a backend deployment option based on its security controls, authentication integration, scaling behavior, operational effort, data region, observability, and current price. There is no single hosting choice or production configuration established here; the important architectural requirement is a developer-controlled server-side credential boundary.
5. Set operational controls before launch
Decide how the service will limit and monitor use, protect credentials, and respond to failures. The API documentation covers rate limits, but there is no universal production limit or cost figure that applies to every app.
- Set per-user and global usage limits appropriate to the feature.
- Monitor request failures and usage costs so unusual activity is visible.
- Avoid logging conversation content that is not necessary for operations, troubleshooting, or a clearly disclosed product purpose.
- Define how server credentials are stored, rotated, and revoked.
- Make error handling distinguish between an app-side connection problem and a provider-side failure where practical.
Test the complete path before release, including lost network connections, timeouts, malformed or empty responses, repeated submissions, authentication failures, rate limits, and abusive input. This is a release checklist, not a claim that any particular implementation has passed those tests.
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6. Map data use, retention, and disclosures
Treat user messages and AI responses as data that may be transmitted to a third party. Map what the app collects, sends, stores, logs, and shares, including data handled by the backend, the AI provider, and any SDKs. Minimize collection and explain the feature’s data use before processing where required. Applicable legal and store obligations depend on the jurisdiction, audience, data, and implementation; this guidance is not a legal determination.
Understand provider data controls precisely
OpenAI’s API data-controls documentation states that, as of March 1, 2023, API data is not used to train or improve models unless a customer explicitly opts in. That statement does not mean that no data is stored. The same documentation describes abuse-monitoring logs, which may contain customer content and are retained for up to 30 days by default unless longer retention is legally required, as well as API features that persist application state. Check the provider’s current data controls and the behavior of the specific endpoint and configuration you use.
For an Apple App Store release
Apple requires developers to provide privacy-practice information in App Store Connect for new apps and updates. Apple’s privacy guidance also addresses privacy manifests describing third-party code practices, including SDK data collection and tracking. Its generative AI guidance advises developers to understand third parties’ privacy approaches, disclose how the app and model use and store personal information, and let users make informed choices about what they share. Make the App Store information, privacy notice, and app behavior consistent with the data flow you have mapped.
For a Google Play release
Google Play makes developers responsible for the user-data practices of third-party code, including AI integrations. Its user-data policy addresses disclosure, consent, and privacy policy duties; its SDK requirements say developers may be asked to demonstrate that SDK data collection meets prominent-disclosure and consent requirements. Inventory the SDKs and AI services in the app and ensure disclosures reflect their actual network and storage behavior.
7. Release checklist
- The provider secret exists only on a protected server-side boundary.
- The backend authenticates users, validates requests, and applies usage limits.
- The app handles loading, errors, retry behavior, and conversation reset or clearing as designed.
- The integration follows the provider’s current API contract, including any streaming behavior used.
- The team has tested network failure, timeout, malformed and empty responses, repeated submissions, authentication failure, rate limits, and abusive input.
- Data collection, transmission, storage, logging, and sharing have been mapped across the app, backend, provider, and SDKs.
- Apple App Store or Google Play disclosures match actual behavior, and users can make an informed choice about what they share.
Recheck current API and store requirements at release time; provider endpoints and platform policies can change.
Frequently Asked Questions
Can I call an AI API directly from my iOS or Android app?
The safer architecture is to send requests through a backend you control. A provider secret embedded in a mobile app can be extracted and used by others.
Does “not used for training” mean the provider stores no chatbot data?
No. Training use, abuse-monitoring logs, and application state are different data-control questions. Review the provider’s current documentation for the specific endpoint and configuration.
Do I need to disclose an AI SDK if it is supplied by a third party?
Using a third-party SDK does not remove your responsibility for the app’s data practices. Apple and Google require developers to account for third-party code and keep disclosures aligned with actual data handling.
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