AI product development
How to Add AI to an Existing Mobile App
How to Add AI to an Existing Mobile App requires decisions about a server-side AI gateway, mobile latency, streaming UI, authentication, cost controls and graceful fallbacks. This guide explains the architecture, delivery and production practices needed to achieve an AI feature that fits the existing iOS or Android journey without exposing provider credentials.
Define the AI job before choosing a model
Write the user request, the information available, the expected output and the consequence of a wrong answer. A narrow job such as classifying a support ticket or drafting from approved records is easier to evaluate than an assistant expected to handle every question.
Set refusal and escalation rules before implementation. The product should tell users what the AI can do, show when information is uncertain and make a person reachable when the task affects money, health, access or another high-impact decision.
Put the AI gateway on the server
A mobile app should call an authenticated backend that owns provider credentials, prompt versions, model routing, rate limits and logging. Shipping an AI provider key in an iOS or Android bundle allows extraction and unauthorized usage.
Design for model latency with streaming, cancellable requests and useful progress states. Validate structured outputs on the server and provide a conventional fallback when the provider is unavailable or the model cannot complete the task safely.
Keep authentication and secrets at trusted boundaries
The backend should own credentials, session validation and privileged integrations. Browser and mobile clients may store only the tokens needed for their session using platform-appropriate protections, and every data request still needs server-side authorization.
Plan expiry, refresh, logout, revocation and compromised-device response. A valid identity does not automatically grant access to another organization's record.
Evaluate before and after launch
Build a dataset of representative, difficult and adversarial cases before tuning prompts. Score factual correctness, task completion, citation support, refusal behavior, latency and cost; a fluent answer is not evidence that the system worked.
Store traces with the model and prompt version, retrieved evidence and tool results, subject to privacy rules. Add production failures to the regression set so provider or prompt changes cannot quietly reintroduce them.
Model build cost and operating cost separately
Implementation includes workflow design, data preparation, prompts, tools, evaluation, safety, integration and monitoring. Monthly operations include input and output tokens, embeddings, vector storage, queues, observability and the people who review failures.
Estimate requests per active user and tokens per successful task. Smaller models, caching, shorter context and deterministic code for simple steps can reduce cost, but every optimization should be checked against the evaluation set.