# AI Integration: Add AI to Your Existing Product or Business

> AI integration services for existing products and systems: OpenAI, Gemini, Claude or local models built into your app — with the architecture, evaluation and cost control to run it in production.

Adding an API call to a model takes an afternoon. Integrating AI so that it is reliable, affordable and maintainable inside a real product takes engineering: deciding where the model sits in your architecture, what data it may see, how its output is validated, and how you will know when it gets worse.

## Typical integrations

- **ChatGPT / OpenAI integration** for drafting, summarising, classification or extraction inside your app.
- **Search and question answering** over your product's data, using retrieval (RAG).
- **AI features in SaaS products** — assistants, smart forms, recommendations.
- **Model choice and switching** between OpenAI, Gemini, Claude and local models, so you are not locked to one provider.

## How I approach it

1. **Find the feature worth building** — where AI improves the product measurably.
2. **Design the integration** — API boundaries, data access, prompts as code, fallbacks.
3. **Build it into your stack** — Go, PHP/Laravel, TypeScript/React, or mobile.
4. **Evaluate and monitor** — test sets, quality checks, latency and cost per request.

I have spent years building production systems in exactly these stacks — Laravel ecosystems ([TomatoPHP](/en/projects/tomatophp), [Laravilt](/en/projects/laravilt)), Go ([ToGO](/en/projects/togo)) and high-traffic platforms such as [iStoria](/en/projects/istoria), which I took over at 2.5M users.

Deciding what to build first? See [AI consulting](/en/ai/consulting). Data that cannot leave your infrastructure? See [Private AI](/en/ai/private-ai).

## FAQ

### Which AI provider should we use?
It depends on the task, language, data rules and budget. I design integrations so the provider can be swapped, and compare models on your own test cases before choosing.

### Can AI be added to a legacy system?
Usually, yes — through a service that sits beside the legacy system and talks to it through its existing APIs or database, without a rewrite.

### How do you keep costs predictable?
By measuring cost per request from the start, choosing the smallest adequate model, caching, and setting limits per user or per feature.

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Source: https://fadymondy.com/en/ai/integration