How to Integrate AI Into an App: A Step by Step Guide

What integrating AI into an app actually involves

Adding AI to an app you already run is an integration project, not a research project. The models exist and are available by API. The work is deciding what the feature should do, connecting the model to your data safely, designing the interaction so people trust it, and measuring whether it helps. Most of the failures Tepia sees come from skipping the first and last of those.

Tepia has added AI features to mobile and web apps across healthcare, field service, retail, ministry and wellness products, using OpenAI and Anthropic model APIs, retrieval augmented generation and vector databases. Tepia scopes this as a contained feature on your existing codebase, which is why it typically takes 6 to 12 weeks rather than the 4 to 10 months of a new product. The add AI to an existing app page covers the engagement itself; this guide covers the steps.

AI feature ideas by app type, with what Tepia typically builds

The best first AI feature is the one that removes a step people already complain about. The table lists proven starting points by app type, the technique behind each, and a typical Tepia timeline for the feature alone.

Tepia will tell you which of these fits your data and your users during Discovery, and which ones you should not build yet because the data is not ready.

How to integrate AI into an app in ten steps

These are the ten steps Tepia follows on every AI integration. They map onto Tepia’s Discovery, Design, Development and Testing, Launch and Support phases.

1. Pick one use case and define success. Choose a single task people do often and write down the metric that will prove the feature works, such as minutes saved per job or support tickets deflected. Tepia refuses to start on a vague goal like “add AI”.
2. Audit the data you already have. List the documents, records, images and events the feature will need, where they live, who owns them and what is sensitive. Tepia’s Investigation Summary covers this in the first two weeks.
3. Tepia defaults to commercial APIs with no training on your data.
4. Design the interaction and the failure states. Wireframe where the feature lives, how it shows uncertainty, how people correct it and what happens when the model is wrong. Tepia designs the error state before the happy path.
5. Build the retrieval layer. Chunk and embed your content into a vector database so the model answers from your data, not its memory. Tepia builds retrieval augmented generation with source citations users can check.
6. Write and version the prompts. Treat prompts as code: stored in the repository, reviewed, tested and versioned. Tepia keeps a prompt changelog alongside the app changelog.
7. Add privacy and safety controls. Redact PHI and PII before requests leave your system, log every request with an audit trail, set rate limits and filter outputs. Tepia signs BAAs with model vendors where HIPAA applies.
8. Build an evaluation set. Collect 50 to 200 real examples with expected answers and score every prompt or model change against them. Tepia runs this as part of its functional test plan.
9. Ship behind a feature flag to a Beta group. Release to a small set of users, watch the metric from step one, and collect corrections. Tepia’s Beta schedule typically runs two to four weeks.

How Tepia approaches AI integration

Tepia runs AI integration through the same six phase process as every project, compressed for a contained feature. Discovery covers steps one through three: a system investigation of your app and data, user interviews with the people who will use the feature, and a third party integration review of model vendors and your existing services. Deliverables are an Investigation Summary, an Interview Summary and User Stories, typically in 2 to 4 weeks for an AI feature.

Design covers step four with detailed wireframes and sample designs for the AI interaction, including empty, loading, uncertain and wrong states. Development and Testing covers steps five through eight, with Alpha and Beta schedules and a test plan that includes the evaluation set alongside functional, user acceptance and non functional tests. Launch covers step nine with a flagged rollout and Tepia support reps monitoring.

Your team is US led throughout. A Tepia project manager, design lead and engineering lead are US based, and the engineers on the feature are hand picked and work in your hours. The full process is on the Tepia process page.

Frequently asked questions