Hiring an AI app development company: features that matter, RAG vs fine-tuning, cost and privacy

Adding a chat box is easy. Shipping an AI feature users trust, that passes store review and doesn't blow up your cloud bill, takes engineering. Here's what to build and what to ask.

Almost every agency now calls itself an AI app development company. Adding a chat box that calls a large language model takes an afternoon. Building an AI feature that users trust, that doesn't leak data, that passes App Store and Google Play review, and that doesn't quietly double your cloud bill takes real engineering.

This guide is for founders and product leads who want to add AI to a mobile or web app. It covers which features are worth building, when to use retrieval (RAG) versus fine-tuning, how to keep model costs under control, what the stores now require on privacy, and the questions that separate a serious AI development partner from a wrapper shop.

Start with the job, not the model

The AI features that survive contact with real users solve a narrow, frequent problem. "Chat with our app" rarely does. Here's how common features compare:

A good test: if you removed the AI, would users notice within a week? If not, the feature is decoration.

Prompting, RAG or fine-tuning: which one you need

These three are often presented as alternatives. In practice they stack, and most apps need only the first two.

• Prompting with a hosted model. Instructions, examples and structured output formats sent to a model from OpenAI, Anthropic, Google or an open-weight model you host. Start here. It's the fastest way to learn whether the feature works.
• Retrieval-augmented generation (RAG). Your content is split into chunks, indexed (usually in a vector database or a search engine with vector support), and the most relevant pieces are added to the prompt at query time. Use it when answers must come from your own, changing data. It also lets you show sources and respect per-user access rights.
• Fine-tuning. Training a model further on your examples. Use it when you need a consistent style or format at high volume, or want a smaller, cheaper model to match a larger one on one narrow task. It does not reliably teach a model new facts, and you'll need to retrain as data changes.

The architecture behind a reliable AI feature

The model call is the smallest part of the system. A production AI feature in a mobile or web app usually has:

1. A backend gateway. The app never calls the model provider directly, and API keys never ship inside the app. Your server handles authentication, rate limits, logging and model selection.
2. Retrieval and data pipelines that keep your index in sync with source content and apply user permissions.
3. Guardrails: input checks, output filters, structured output validation and refusal handling for topics you don't want the feature to touch.
4. An evaluation set. A few hundred real questions or documents with known good answers, run against every prompt or model change. Without it, every "improvement" is a guess.
5. Observability: logs of prompts, retrieved sources, outputs, latency, cost and user feedback, with personal data redacted.
6. Fallbacks for provider outages, timeouts and low-confidence answers, including a route to a human where it matters.

Keeping model costs under control

AI features have a cost per use, unlike most app features. A popular feature with no limits can turn a healthy margin negative. The levers:

• Route by difficulty. Send simple requests to a small, cheap model and escalate only hard ones to a large model.
• Trim the context. Retrieve fewer, better chunks. Long prompts cost money on every single call.
• Cache. Reuse answers to repeated questions, and use provider-side prompt caching for long, fixed instructions.
• Cap usage per user and tier. Free users get a daily limit; paid plans get more. Tie limits to your pricing.
• Batch non-urgent work such as overnight document processing, which many providers price lower.
• Track cost per active user every week next to revenue per active user. If you can't see it, you can't manage it.

Model prices change often, so build your gateway so you can switch providers or models without an app release.

Privacy, data handling and store policies

Both app stores now have AI-specific rules, and they're enforced at review.

• Apple: Apple's App Review Guideline 5.1.2(i) requires apps to clearly disclose where personal data will be shared with third parties, including third-party AI, and to get explicit permission before doing so. In practice, that means a clear consent step before sending user content to an external model, and a privacy policy and App Privacy labels that match.
• Google Play: the AI-generated content policy covers apps that generate text, images or video from prompts. They must prevent restricted content and let users report or flag offensive output without leaving the app.
• Vendor terms: check whether your model provider uses API data for training, how long it retains inputs, and whether it offers zero-retention or regional data options. Get this in writing for enterprise customers.
• Regulated data: health, financial and children's data bring HIPAA, GLBA, COPPA or GDPR obligations. That can mean a business associate agreement with the model vendor, self-hosted models or keeping certain fields out of prompts entirely. This is general guidance, not legal advice.

If you're building in healthcare, read our guide to healthcare app development before you choose a model vendor.

What to ask an AI app development company

1. Show me an AI feature you've shipped to real users. What did you change after launch, and why?
2. How will you evaluate quality before and after launch? What's in the evaluation set?
3. Where do API keys live, and how is user data redacted from logs?
4. What will this cost per active user per month at our expected usage, and what are the levers if it's too high?
5. Can we switch model providers without rewriting the app?
6. How will you handle Apple's third-party AI consent and Google Play's reporting requirement?
7. Who owns the prompts, evaluation data, fine-tuned models and code? (You should.)

If a team can't answer the evaluation and cost questions concretely, they're selling a demo, not a product. Our broader guide on how to choose an app development company covers contracts and red flags.

Timeline and budget

As typical ranges for an offshore team: adding one well-scoped AI feature (for example, answers from your help content, or document extraction) to an existing app often takes four to eight weeks including evaluation. A new AI-first MVP usually takes eight to fourteen weeks. On top of the build, budget for model usage, vector database or search hosting, and monitoring, all of which scale with usage. Our MVP development guide covers how to scope the first release.

Building AI features with Appryze

Appryze builds Android, iOS (native Swift or Flutter) and web apps with AI features behind a backend gateway you own, with evaluation sets and per-user cost tracking from the first release. You get a fixed-price written quote within 24 hours, weekly written updates, and free App Store and Google Play submission, including the AI privacy disclosures reviewers now check. Start on our Studio page.

Frequently asked questions

What does an AI app development company do?

It designs and builds apps where AI is part of the product, such as answering questions from your content, summarizing documents, extracting data from images or drafting text. Beyond calling a model, a good partner builds the backend gateway, retrieval pipeline, guardrails, evaluation set and cost monitoring, and handles app store privacy requirements so the feature works reliably for real users.

Should I use RAG or fine-tune a model for my app?

Use RAG when the model needs your own, changing information, such as product details, policies or user documents, and when you need to show sources or respect access rights. Fine-tune when you need consistent format or tone at high volume, or want a smaller model to handle one narrow task cheaply. Many apps need only good prompts plus RAG.

How much does it cost to add AI to an app?

As a typical range, adding one well-scoped AI feature to an existing app with an offshore team takes about four to eight weeks of development, and a new AI-first MVP about eight to fourteen weeks. On top of that you pay ongoing model usage, hosting for search or vector databases, and monitoring, which grow with usage. Prices vary widely by scope.

Does Apple allow apps that send user data to ChatGPT or other AI models?

Yes, with conditions. Apple's App Review Guideline 5.1.2(i) requires apps to clearly disclose where personal data will be shared with third parties, including third-party AI, and to obtain explicit permission before sharing it. Your privacy policy and App Privacy details must also match what the app actually does with user data.

How do I stop AI features from getting too expensive?

Send simple requests to smaller models and escalate only hard ones, retrieve fewer but better context chunks, cache repeated answers, and set usage limits by plan. Track cost per active user weekly against revenue per active user. Build your backend so you can switch models or providers without releasing a new app version.

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