AI MVP development from your idea to a launch-ready product
For founders, domain experts, and small teams with an AI product idea. I build the MVP with sign-up, payments, and the core AI workflow working, then launch it on your domain.
13+ years shipping web products, independent since 2015. I took my own SaaS, RankMySEO, from idea to a hosted MVP.
What AI MVP development with me looks like
AI MVP development means building the smallest version of your AI product that real users can sign up for. I build the one AI workflow your idea depends on and test its output on sample cases. Then I wrap it in login, payments, usage limits, and analytics, and launch it on your domain.
I build on existing models, mostly with Next.js and TypeScript. If your idea needs a model trained from scratch, you need an ML research team first. If you already have a product and want one AI feature in it, see AI integration services.
Who I build AI MVPs for
You bring the problem and access to the people who have it, and I handle the build.
Founders with an idea
You know the problem and who would pay to have it solved. I turn the idea into a product people can sign up for, so you test it with real users.
Domain experts
You know an industry and its daily tasks better than any developer. You bring the workflow, and I handle the code, the model calls, and the hosting.
Small teams
You have a team, but nobody is free to build the AI part. I build the MVP in a repo your developers can take over after launch.
What a launch-ready MVP includes
A demo that works on my laptop is a prototype. Launch-ready means a stranger can find it, sign up, use the AI workflow, and pay, and you can see what they did. This is the list I check before launch day.
- Real sign-up and login, so each user has an account and their own data
- Payments, if the product charges from day one
- The core AI workflow working on real inputs from real users
- An evaluation set: sample cases I check the AI output against before each release
- Guardrails on what the model sees, what it may do, and what it returns
- Usage limits per user, so one account cannot run up your model bill
- Analytics that show who signs up, who comes back, and which steps they use
- Deployed on your domain, with the code and prompts in your own repo
What I cut from v1, and why
Every extra feature delays the day you learn whether people want the product. Cut items go on a written list, so nothing gets lost.
A second AI workflow
One workflow that works tells you more than three that half work. The second one waits for usage data.
Native mobile apps
A responsive web app already runs on phones. Native apps make sense once people use the product often enough to ask.
Team accounts and roles
Most MVPs start with single users. Roles come back when a paying team asks for them.
Fine-tuned or self-hosted models
An existing model with good prompts and your data is usually enough to test the idea. Custom models can come later, if ever.
Every integration on the wish list
I connect the one or two tools the core workflow needs. The rest go on a list, ranked by what users request.
A full admin panel
Early on, I answer admin questions straight from the database. A panel is worth building once the same support task repeats.
How I keep AI cost per user small
An AI product can lose money on every user if nobody watches the model calls. I build it the same way I build SaaS that is cheap to run.
Call the model only when needed
Plain code handles parsing, filters, math, and formatting. The model runs only on the step that needs judgment or language.
Cache answers
When a request repeats, the stored answer comes back without a new model call. Repeat questions stop costing you money.
Smaller models for simple steps
Tagging, routing, and short extractions go to a small, cheap model. The larger model handles the step where quality shows.
Spend caps per user
Each account has a usage limit, and free plans get a lower one. A single heavy user hits the cap before your bill grows.
Hosting that scales from zero
The app runs on serverless hosting such as Vercel, so a quiet week costs little. Hosting cost grows only when people use it.
Cost per user, measured
Every model call is logged with its user and feature. You see what an active user costs before you set your prices.
Proof you can check before you hire me
I took RankMySEO from idea to a hosted MVP
RankMySEO is a SaaS I built myself. It keeps daily Google rank history, runs rank checks in the background, and has Overview, Keywords, Competitors, and Insights views. I proved it on the diurusin.id demo, then opened the public beta on 31 Aug 2026. The story is in introducing RankMySEO and the open beta post.
It gives AI assistants tools, with approval gates
RankMySEO exposes tools that AI assistants can call. In the open-source toolkit, the agent tools sit behind approval gates. I use the same pattern in an AI MVP: the agent asks a person before a risky step.
I pick the model per task
I am the first Cursor Ambassador in Indonesia, a SpaceXAI Ambassador, and a MiniMax Dev Community Expert, so I work with several model providers. Each step in your MVP gets the model that fits it. The provider sits behind one layer, so you can switch later.
Is an AI startup MVP the right next step?
If your idea works better without AI, I will tell you on the first call.
Good fit
- You can describe the one task your product does for a user in two sentences
- You have real examples of the inputs: documents, messages, records, or questions
- You want real users and usage data before you raise money or hire a team
- You have a no-code prototype and need it rebuilt as code you own
Look elsewhere
- You need a model trained from scratch or ML research
- You want a pitch deck, investor intros, or help raising money
- You want every feature on the wish list in the first version
- You want your own developers to learn to build with AI (that is Cursor mentoring)
Want your team to build it themselves? See Cursor mentoring.
How we build an AI MVP together
You see working software every week, so you can change course before the budget is spent.
Idea call
The idea, the users, and the one result they would pay for
One-page scope
The one workflow that must work, what is cut, and how we judge AI output
Prototype on real data
The AI step running on your real inputs, so you judge it by using it
Build with weekly demos
Sign-up, payments, limits, and analytics, shown to you every week
Launch
On your domain, with the evaluation set passing and spend caps on
Decide what is next
Usage data shows what to build, fix, or cut in version two
AI product development guides and related services
What I have written about building with AI, next to the services that put it into practice.
AI development services
Agents, chatbots, and LLM features for products that already exist.
AI integration services
One AI feature added to an app or business you already run.
SaaS development
Web apps built to stay cheap to run as the user count grows.
Fix AI-written code
For prototypes built with AI coding tools that break once real users arrive.
Introducing RankMySEO
Why I built my own SaaS and what the first version does.
RankMySEO is live in open beta
What was ready when the public beta opened.
AI agent development
How I build agents that ask a person before risky steps.
When a website needs machine learning
How to tell whether your idea needs a model at all.
AI MVP development FAQ
How much does AI MVP development cost?+
I scope and price each MVP after a call, because the cost depends on the core workflow, your data, and what launch needs. Send me the idea and who it is for. I reply with what I would build first, what I would cut, and a price for that scope.
Which AI model will my MVP use?+
The one that fits each step, because I am not tied to one provider. I work with OpenAI, Anthropic, MiniMax, and other compatible APIs, often through the Vercel AI SDK on Next.js. The provider sits behind one layer, so switching later does not mean a rewrite.
Who owns the code and the prompts?+
You do. The code, prompts, and evaluation cases live in your repo from the first commit, and the app runs on accounts in your name. If we stop working together, you keep all of it.
What happens after launch?+
We look at the usage data and decide what to build, fix, or cut next. I can keep working on it as a retainer, or hand it to your team with notes on how the pieces fit together.
Can you work from my no-code prototype?+
Yes. A no-code prototype is a useful spec, because it shows the workflow you want. I rebuild it as code you own and keep the flows your users already like. If your prototype was written with an AI coding tool and keeps breaking, my code fixing service may be the cheaper first step.
Can you help me raise money or pitch investors?+
No. I build the product, and I do not fundraise, write pitch decks, or make investor intros. A launched product with real users and usage data is something you can show anyone you pitch.
Have an AI product idea you want to launch?
Send me the idea, who it is for, and the one task it must do well. I reply with what I would build first, what I would cut, and how we would test the AI output. If the idea does not need AI, I will say so.