AI Integration: Add AI to Your Existing Product or Business

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, Laravilt), Go (ToGO) and high-traffic platforms such as iStoria, which I took over at 2.5M users.

Deciding what to build first? See AI consulting. Data that cannot leave your infrastructure? See 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.