How to Add AI Features to an Existing App

Choose the Feature Before the Model

The most successful AI features are narrow and boring in the best sense: they remove a step users already dislike. Good candidates in business apps include drafting a first reply to a customer message, summarizing a long job history before a service call, turning a photo of a receipt into structured data, suggesting the right category or priority for a support ticket, and natural-language search over your catalog or help center.

Weak candidates are features where a wrong answer is costly and hard to spot, such as medical, legal or financial advice delivered directly to consumers, or anything where AI acts on the user's behalf without confirmation. Those can be done, but they need far more design, testing and oversight. If your team is still deciding what to automate, our guide to running an AI pilot project is a good companion.

Architecture: Cloud, On-Device and the Layer in Between

Cloud models

Hosted models from providers such as Anthropic, OpenAI and Google offer the strongest reasoning and language quality. They charge by usage (tokens processed), add network latency, and mean user data leaves the device. Review each provider's business terms on data retention and whether API data is used for training.

On-device models

Apple's Foundation Models framework gives apps access to an on-device language model on supported devices, and Android offers Gemini Nano on supported devices. On-device models are well suited to summarizing, extracting and rewriting short text privately and offline, with no per-request fee, but they are smaller and less capable than cloud models and depend on the user's hardware.

Your backend layer

Whatever you choose for cloud calls, do not call the provider directly from the app with an embedded key; it will be extracted and abused. Put a thin service in between that authenticates the user, enforces per-user limits, assembles the prompt with retrieved data, logs inputs and outputs for quality review, and can switch models without an app release. That layer is also where you apply your existing authorization rules, so the AI never sees records the user could not see themselves.

Making Quality Measurable

A feature that looks impressive in a demo can fail on real inputs. Before launch, build an evaluation set from realistic examples: typical requests, ambiguous ones, out-of-scope questions and attempts to misuse the feature. Decide how each output will be graded, by exact match, a checklist or a reviewer's score, and set a pass threshold. Rerun the set whenever you change the prompt, the retrieved data or the model. Our guide to evaluating AI output quality covers the method in detail.

In production, collect lightweight signals: thumbs up or down, whether users edited the draft heavily, and how often they abandoned the feature. Those tell you where to improve far faster than anecdotes.

Security, Privacy and Store Rules

AI features introduce new risks. The OWASP Top 10 for LLM Applications puts prompt injection first: instructions hidden in user input or retrieved documents that try to override your rules. Mitigate it by limiting what the model can do, treating model output as untrusted input, requiring user confirmation for actions, and never putting secrets in prompts. Other listed risks include sensitive information disclosure and unbounded consumption, which is a polite name for a surprise bill.

Platform rules apply too. Apple's App Store Review Guidelines require apps to clearly disclose where personal data will be shared with third parties, including third-party AI, and to obtain explicit permission first. Google Play's AI-Generated Content policy requires apps that generate content with AI to prevent prohibited content and include in-app reporting so users can flag offensive output. Update your privacy policy, Apple's privacy details and Google's Data safety form to match. For a governance framework, NIST's AI Risk Management Framework and its generative AI profile are free and practical.

Controlling Cost and Latency

Usage-based pricing scales with your users, so estimate cost per active user before launch: average requests per user, tokens per request, and the model's current price. Then add controls: per-user daily limits, caching of repeated answers, shorter prompts, smaller models for simple steps, and streaming responses so users see output immediately. Many teams route easy requests to a fast, inexpensive model and reserve the most capable model for hard ones. Revisit the numbers monthly, because both your usage and provider pricing change.