How to Add ChatGPT, Claude or Gemini to Your Existing App

You do not need to rebuild your app to use AI. Most useful AI features can be added to an existing mobile app, website or internal tool by adding one well-designed backend layer. This guide walks through the steps we follow and the decisions that matter most.

1. Start from a user problem, not a model

"Add ChatGPT" is not a feature. Good AI features solve a specific problem that users already have:

• Users cannot find the right product or article, so add semantic search.
• Support answers the same questions daily, so add an in-app assistant grounded in your help content.
• Users upload documents or photos, so add extraction and summarisation.
• Staff write the same kind of text repeatedly, so add drafting with human review.

Pick one, define what "good" looks like, and measure it.

2. Never call the AI provider directly from the app

The most common mistake we see is an API key placed inside a mobile app or website code. Anyone can extract it and run up your bill. Instead, the app calls your backend, and your backend calls OpenAI, Anthropic or Google. This layer is also where you:

• check who the user is and what they are allowed to see,
• add your instructions and business rules to every request,
• limit usage per user or per plan, and
• log requests for debugging and cost tracking.

3. Choose the model by testing, not by brand

OpenAI's GPT models, Anthropic's Claude and Google's Gemini are all capable. They differ in cost, speed, context length, handling of images and documents, and how they follow instructions. The practical approach is to build a small test set of 30 to 50 real examples from your app, run them through two or three candidate models, and compare quality, speed and cost. Often a smaller, cheaper model is enough for simple tasks, with a stronger model only for the hard ones.

Because the model is called from your backend, you can switch providers later without releasing a new app version.

4. Ground answers in your own data

If the feature needs to know about your products, policies or the user's records, use retrieval-augmented generation (RAG): store your content as searchable embeddings, find the most relevant pieces for each question, and give only those to the model with an instruction to answer from them. This keeps answers specific and current, and lets you show the source.

5. Design for wrong answers

AI will sometimes be wrong. Plan for it:

• Show sources where possible, so users can check.
• Ask for confirmation before any action that changes data or spends money.
• Use structured output (fixed fields) when the result feeds into another system, and validate it.
• Send low-confidence cases to a person.

6. Control the running cost

• Send only relevant context, not entire documents, with each request.
• Cache answers to repeated questions.
• Use smaller models for classification, extraction and short answers.
• Set per-user limits, especially on free plans.
• Track cost per feature, not just the total bill.

7. Release small, then improve

Launch the feature to a small group of users first. Watch real usage, read the logs and fix the cases where answers were poor. Then expand. Keep your test set and re-run it whenever you change a prompt or model, so improvements are measured rather than guessed.

Summary

Adding AI to an existing app comes down to one clear use case, a secure backend layer, the right model for the job, grounding in your own data, guardrails for mistakes and attention to cost. Done this way, AI becomes a dependable feature rather than a demo.