Add AI to your product without breaking what works

Read your code and data

Before touching anything, I read your codebase, data model and exactly where the feature has to plug in. No blind changes to a product that's already earning.

Scope the AI where it earns its place

We pick the one spot where AI actually helps — a chatbot, smarter search, or recommendations like eRENTAL's — instead of adding AI for its own sake.

Build it in isolation

The feature ships behind its own boundary, with guardrails and an admin view, so it can't break the flows you already depend on.

Test and roll out to production

Tests on the new path, then a controlled release to your live product — with a way to switch the feature off if anything misbehaves.

Right when you already have a live product and want to add one capability — AI or otherwise — without a rewrite or a risky big-bang release.

If you just need a generic support chatbot on a standard site, an off-the-shelf tool (Intercom Fin, Crisp, or a no-code GPT widget) is faster and cheaper than custom. Custom earns its keep when the AI has to reason over your own data, rules and workflows — where a drop-in bot can't reach.

Adds a feature or AI to a product you already run — without breaking what works.

• ✓Reading your code and data before touching anything
• ✓The feature in isolation: won't break what already works
• ✓AI where it fits: smart search, drafts, parse-by-link
• ✓Guardrails and an admin view for the new function
• ✓Tests and rollout to your production

Will adding AI break my existing app?

No — the feature ships in isolation, behind its own boundary, with guardrails and an admin view, so it can't reach the flows you already rely on. I read your code and data first, work on a copy or staging, add tests on the new path, and keep a switch to turn the feature off. Your live product stays working the whole time.

Which AI features are actually worth adding?

The ones that use your data: a chatbot that answers from your content, forgiving smart search, recommendations, draft generation, or parse-by-link. On eRENTAL, recommendations are scored by shared category, brand and price tier, and an assistant turns a product link into a ready draft. Where a plain rule or filter does the job, I'll tell you AI isn't worth it.

How much does the AI itself cost to run each month?

The build is one-off; the AI's running cost is separate. Expect roughly €15–300 per month for the LLM API plus hosting, scaling with usage. On free-tier models like Gemini, Groq or Cerebras it can be near zero at lower volumes. I size it for your traffic up front, so there are no surprises on the invoice.

Do you need access to my codebase?

Yes — I read your code and data model before touching anything, so the feature fits how your app actually works. I can work on a copy, a branch or a staging environment rather than production, and an NDA is fine. Nothing reaches your live product until it's tested and you've signed it off.

How do you stop the AI from hallucinating?

Guardrails, retrieval over your own data instead of the model's guesses, and human-in-the-loop where it matters — on eRENTAL the AI drafts a product, a person always approves before it goes live. New AI functions get an admin review view. No model is 100% reliable, so I design the feature to fail safe, not to pretend it's perfect.