From idea to MVP with AI in weeks, without burning your budget

You have an idea, a budget that isn’t infinite, and an uncomfortable question: does the thing I’m imagining matter to anyone enough that they’d pay for it? The expensive way to find out is to build for six months and pray. The sensible way is to build an MVP with AI that puts your hypothesis in front of real users in weeks, not quarters, and that costs what it should cost: not much, while you’re still validating.

I’ve spent years taking AI systems to production —computer vision, LLM agents, digital twins— and the lesson that keeps repeating is this: the risk in a new product is almost never technical, it’s market risk. The MVP exists to retire that risk cheaply. If you build it as though you already had traction, you’re doing it backwards.

What an MVP with AI is (and isn’t)

An MVP is not “version 1.0 with fewer buttons”. It’s the smallest experiment that answers one concrete business question. The word that matters isn’t Minimum, it’s Viable: it has to be real enough that someone actually uses it and tells you the truth through their behavior, not through their politeness.

Adding AI to the equation changes the math in two ways:

• It accelerates the build. A large share of the scaffolding —types, tests, boilerplate, repetitive integrations— gets generated and reviewed in a fraction of the time. That doesn’t replace engineering judgment; it amplifies it. I went into detail in Claude Code and MCP in the development loop.
• It can be the product. If your value proposition is semantic search, classification, generation or assistance, AI isn’t decoration: it’s the core. And there the question stops being “does it work?” and becomes “does it work reliably, at a cost that doesn’t eat your margin?”.

Conflating these two things is expensive mistake number one. Let’s keep them clearly apart.

The cost trap: why an MVP with AI can wreck your economics

Everyone’s first prototype today calls the most expensive API on the market on every single interaction. It works beautifully in the demo with three users. Then it scales, and you discover that each active user costs you real money every time they press a button. The product validates; the unit economics don’t.

Designing for cost from day one is part of the job, not an optimization for “later”. A few decisions I make almost every time:

• A provider chain with a free fallback. Instead of tying myself to a single paid provider, I chain models by cost and availability: something fast and free first, open alternatives next, and I only escalate to premium models when the task justifies it. That’s exactly the pattern I built into DevFlow AI —Groq → OpenRouter → Pollinations— and into this portfolio’s own assistant: if one provider goes down or saturates, the next one answers, and the user never notices.
• On-device AI when you can. Not all AI needs a cloud API. In RecruitSecure AI the semantic search runs 100% in the browser with Transformers.js: the CVs never leave the device. Cost per inference: zero. GDPR compliance: by design, not by policy. When the use case allows it, moving compute to the edge is the difference between a zero marginal cost and a bill that grows with every user.
• Cache what repeats. Much of the “intelligence” an MVP asks for is the same question phrased a thousand different ways. A caching layer and well-used embeddings turn paid calls into near-free reads.

An MVP with AI that validates demand but breaks the unit economics has validated nothing: it has postponed the problem and made it more expensive.

The stack I use to move fast without paying for it later

“Fast and cheap” is usually a euphemism for “technical debt you’ll pay back with interest”. It doesn’t have to be. The key is choosing tools with a high ceiling: ones that let you start over a weekend and don’t force a rewrite once it works.

• Frontend and rendering. Astro or Next.js depending on the case: mostly-static content with interactive islands, or an app with real client state. When the win is content speed and SEO, fast websites with Astro are hard to beat —islands hydrate only what needs JavaScript. TypeScript in strict mode from the first commit —zero any— because the types are the contract that lets AI generate code that actually fits.
• Backend. FastAPI (Python) when there’s ML involved, Node when the weight is on orchestration and integrations. Small APIs, no business logic in the UI, no coupling the domain to the framework.
• Data. Postgres as the workhorse; Qdrant or another vector database when there’s semantic search; Redis for caching and rate-limiting. Nothing exotic unless the problem demands it.
• Deploy. Vercel for most MVPs: a preview on every change, one-click rollback, zero servers to maintain while you validate. When the product needs more control, you migrate; but paying for a container orchestrator to serve twenty users is burning money and time.

This isn’t a toy stack. It’s the one I build production-grade things with —and the same one this portfolio you’re reading is built on.

How I work: the phased process

A cheap MVP is cheap because it’s scoped, not because it’s badly built. The process I follow has three phases with one deliverable each, so at no point are you paying blind.

Phase 1 — Validation and scope (days, not weeks)

Before writing any code, we define the business question and the minimal experiment that answers it. We leave with a written scope: what’s in, what’s out (just as important), which metric decides whether the MVP worked, and where the real risk sits. If the idea doesn’t need AI, I’ll tell you. Selling AI that adds nothing is the fast way to burn your budget and my reputation.

Phase 2 — Building the core

I build the one thing that makes the product valuable first —the heart of the proposition— and wrap it in the bare minimum a real user needs to use it end to end. AI-assisted development for the scaffolding, human review for everything that matters, and incremental deliveries you can see running: no disappearing for a month and reappearing with a black box.

I know how to build under pressure and with focus: BusAvanza, a working prototype with an interactive map and a points system, we stood up in 48 hours and it placed 4th among more than 50 teams at a hackathon. A client MVP gets more time and more care, but the mindset —land the idea, make it tangible, defend it— is the same.

Phase 3 — Measurement and decision

An MVP without instrumentation is a hunch with better design. I wire analytics from the first deployment so the data makes the decisions. By the end of this phase you have an honest answer to three questions: are people using it? are they using it the way we expected? how much does it cost to serve? With that you decide: iterate, pivot or stop. All three are valid outcomes. The only bad option is not knowing.

Indicative packages

Every idea is different, but to give you a picture of the journey, this is how I usually structure the work:

• Validation sprint. A closed scope, a navigable prototype of the central piece, and a “this makes sense / this needs rethinking” decision before committing the big budget.
• Full MVP. A working end-to-end product, with its AI core, deployed, instrumented and in real users’ hands. AI cost controlled by design.
• Post-launch iteration. Improvements guided by the data the MVP itself collects, no rewrites, on a foundation built to grow.

I don’t sell hours: I sell the experiment you need to decide with data instead of faith.

What I won’t do

For the sake of honesty, and because it saves grief: I won’t over-build. I won’t set up microservices for a product that has no users yet, nor put AI where a database query solves it better, nor promise you metrics no one can promise before launch. A good MVP is an exercise in discipline: doing less, but doing well the part that decides the product’s future.

Let’s talk about your idea

If you have an idea and want to know whether it holds up —and what it would cost to put it in front of real users without mortgaging the budget— that’s exactly the problem I enjoy. Tell me what you want to validate and I’ll tell you, straight, how I’d approach it, what would go into the MVP and what I’d leave out.

Reach out from the contact section and we start with the right question: not “how do we build it?”, but “what do we need to know first?”.