AI integration services for the product you already run
For businesses and product teams who want one AI feature working inside the app, site, or tools they already have. I add it next to your current code, with spend caps and tested answers before launch.
13+ years shipping web products on Next.js, PHP, and WordPress. First Cursor Ambassador in Indonesia, SpaceXAI Ambassador, and MiniMax Dev Community Expert.
What my AI integration services cover
AI integration services add a model-powered feature to software you already run. I pick one workflow, connect a model to your data through your own server, and ship it with logging, spend caps, and tested answers. You keep your current app, and your team gets code they can read.
This page is about hiring me to do the work. For the technical walk-through, read how AI integration works, step by step. If you have an idea and no product yet, start with AI MVP development.
AI features for business I can add
Most requests fall into one of these. The details are in my guides to building an AI chatbot for business and AI agents.
Search and Q&A over your own docs
Staff or customers ask a question and get an answer drawn from your docs, policies, or product pages, with a link to the source.
Drafted replies for support
The model reads the ticket and your help content, then drafts a reply. A person edits it and presses send.
Summaries and tags on records
Long notes, calls, or tickets get a short summary and consistent tags, so your lists are easier to filter and report on.
Data pulled from documents and invoices
Names, dates, totals, and line items come out of PDFs and scans into your database. Rows the model is unsure about get flagged for review.
An assistant inside your dashboard
Users ask about their own data in plain language. The assistant answers only from what their account is allowed to see.
An agent that runs a workflow
It works through a multi-step task with your tools and APIs. Anything that sends, deletes, or charges waits for a person to approve it.
Add AI to an existing app without a rewrite
A common request is to integrate ChatGPT into a website. I build it so the model call runs on your server, answers come from your content, and each user has a usage cap.
Next.js and React
The usual case. I add the feature inside your repo, often with the Vercel AI SDK, and stream answers into your existing UI.
Next.js developmentWordPress
A plugin or a headless front end calls the model from the server. The API key never reaches the browser, and answers can come from your posts.
WordPress developmentPHP and Laravel
A service class or API route in your PHP app handles the model calls. Your existing auth and database stay as they are.
PHP API developmentAny app with an API
If your system has an API, I build the AI feature as a small service that talks to it. Your main app needs few or no changes.
Data privacy and model training
Before I connect anything, we agree on which data the model may see. I send it only the fields a task needs. Names, emails, or account numbers can be masked before the call.
I check each provider's data terms and choose settings that keep your data out of model training, where the provider allows it. The API key stays on your server. The logs of prompts and answers go to your own database.
Cost control for generative AI integration
Generative AI integration services get expensive when every click calls a model. Plain code handles what it can, and the model runs only on the step that needs it.
Cache repeated answers
Questions that repeat get the stored answer back, with no new model call.
Small models for simple steps
Tagging, routing, and short extractions run on a small model. The larger one handles the step where quality shows.
Spend caps
Each feature and each user gets a limit, so a bug or one heavy user cannot run up the bill.
Cost per action, measured
Every call is logged with its feature. You see what one answer, one draft, or one extracted invoice costs.
Testing AI answers before launch
Before launch, I build a set of real cases from your data: questions, tickets, or documents with the answer you expect. Every prompt or model change runs against that set. We see whether it got better or worse before users do.
When the model is unsure, or the question falls outside your content, the feature says so and hands off to a person. Wrong answers still happen sometimes. That is why risky actions wait for a person to approve them.
How an AI integration rolls out
Small first, so you find out whether the feature pays for itself before every user has it.
Pick one workflow
One task with an outcome we can measure, like time per ticket
Prototype on your data
A working slice on your real records, so you judge it by using it
Ship behind a flag
Live for a few users while everyone else sees the current app
Measure
Answer quality, cost per action, and whether people keep using it
Roll out
Open it to everyone, or change course before more money goes in
Is adding AI worth it for your business?
If a plain feature would do the job better, I will tell you.
Good fit
- One repetitive task eats hours, like answering the same questions or copying data out of documents
- Your docs, records, or tickets already hold the answers your users need
- You run a working app on Next.js, WordPress, PHP, or anything with an API
- You want to try the feature with a few users before everyone gets it
Look elsewhere
- There is no product or data yet (an AI MVP is the better place to start)
- You need a model trained from scratch or ML research
- You want AI on every page with no task in mind
- You want your own developers to learn to build with Cursor (that is Cursor mentoring)
Starting from an idea? See AI MVP development. Want your team to learn it? See Cursor mentoring.
AI integration guides and related services
What I have written about AI features, next to the services that put it into practice.
How AI integration works, step by step
My longer guide to AI integration and what each step involves.
Building an AI chatbot for business
A chatbot that answers from your content and hands off to a person.
AI agent development
Agents that call your tools and ask before risky steps.
Machine learning in a Next.js product
How a model fits into a Next.js app you already run.
AI development services
Agents, chatbots, and LLM features for products that already exist.
AI MVP development
A new AI product built from your idea to launch.
Fix AI-written code
For an app or prototype written with AI tools that now breaks.
PR review checklist for AI-generated code
The checks I run on code written with AI tools before it merges.
AI integration services FAQ
How much do AI integration services cost?+
I scope and price each feature after a call, because a docs search and an agent that touches billing are different work. Send me the task and where your data lives. I reply with how I would build it and a price for that scope. Model usage is billed by your provider, and I measure it per action before rollout.
Which AI model or provider do you use?+
I pick the model per task, and I am not tied to one provider. That usually means OpenAI, Anthropic, MiniMax, or another compatible API, behind one server layer. You can switch providers later without rewriting the feature.
Is my data safe with an AI integration?+
The model only sees the fields a task needs, and the API key stays on your server. I choose provider settings that keep your data out of model training where the provider allows it. Sensitive fields can be masked before the call.
Can you add AI to WordPress?+
Yes. I build WordPress plugins and headless WordPress sites, so the AI feature can live in a plugin or in a Next.js front end. Either way the model call runs on the server, and answers can come from your posts and pages.
What if the AI gives wrong answers?+
It will sometimes, so I plan for it. I test against a set of real cases before launch and show the source with each answer. When the model is unsure, it hands off to a person. Actions that send, delete, or charge wait for approval.
Can my team maintain it after you finish?+
Yes. The code, prompts, and test cases live in your repo, written in the same style as the rest of your app. I hand over notes on how it fits together, and I can stay on a retainer for model updates and fixes.
Have one task you want AI to handle?
Send me the task, the app it lives in, and where the data is. I reply with how I would build it and how we would test it. If plain code would do the job better, I will tell you.