Have a SaaS or AI product idea? You have the idea. You know the problem you want to solve. Maybe ...Have a SaaS or AI product idea? You have the idea. You know the problem you want to solve. Maybe ...
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And before you know it, your “simple
MVP” has become a 6–12 month project.
But here's the important thing:
Your first version doesn't need
everything.
It needs the right things.
A better approach is:
💡 IDEA
↓
🎯 DEFINE
THE PROBLEM
↓
👥
IDENTIFY THE TARGET USERS
↓
📋 DEFINE
THE MVP
↓
🏗️
DESIGN THE ARCHITECTURE
↓
🎨 DESIGN
THE UI/UX
↓
💻 BUILD
THE PRODUCT
↓
🤖
INTEGRATE AI/AUTOMATION WHERE IT ADDS VALUE
↓
☁️ DEPLOY
↓
🚀 LAUNCH
↓
📊
COLLECT USER FEEDBACK
↓
🔄
ITERATE
The objective isn't to build a huge
product on day one.
The objective is to build enough to answer:
“Will people actually use and pay for
this?”
That's where MVPHub comes in.
At MVPHub, we help founders and
businesses turn ideas into working:
·
MVPs
·
SaaS applications
·
AI-powered applications
·
Business automation
solutions
·
Custom web applications
From idea → architecture → development →
deployment → launch.
If you're sitting on a software idea and
don't know where to start, send me a message with:
Before you add AI to your product, find the screen where your users already copy and paste.
That's usually where the real AI feature lives. Not a chatbot in the corner, but a step people already do by hand: summarising a ticket, drafting a reply, tagging an upload, pulling fields out of a PDF.
Here's the order I follow when a founder asks me to add AI to an existing SaaS:
1. Pick one workflow, not "AI everywhere." Watch where users leave your app to paste something into ChatGPT and come back. That round trip is your feature.
2. Keep the model behind your own API. One service owns prompts, retries, logging and cost limits, so you can switch providers without touching the frontend.
3. Make it tenant-aware from day one. Each customer's data, prompts and usage stay scoped to their account, or the first enterprise security review will stop you.
4. Ship a draft, not a decision. Let the AI suggest and the user approve. You earn trust and get real feedback before you automate anything.
5. Measure accepted output, not tokens. If users rewrite most of what it produces, the prompt or the context is wrong, and that's your real roadmap.
Most AI features that flop didn't fail because of the model. They were bolted onto the wrong step.
What's the one task your users still copy into ChatGPT?
One site, two businesses 🏁
Kart Vlak sells off-road karts in Uruguay. They needed to sell directly to families and recruit distributors across the country, without running two websites.
So we designed and built one store in Next.js that does both:
→ Full-screen video hero with a high-energy look that matches the product
→ Product pages with spec bars and a 360° viewer to see the kart from every angle
→ Checkout with bank transfer or Mercado Pago
→ A step-by-step assembly video built into the site
→ A distributor section with its own meeting scheduler
Your website can be your store and your B2B sales channel at the same time.
Full case study on my profile.
putting the assembly video right in the site is smart, that's a support email waiting to happen otherwise. how did you keep the full-screen video hero light on mobile data? that's the part I always end up fighting with
LeadAI — AI Lead Generation SaaS Dashboard
A modern AI-powered lead generation dashboard designed to simplify sales automation, schedule workflows, and help teams manage prospecting activities.
The interface combines a clean SaaS layout, automation analytics, status indicators, and an organized schedule management table.
Project Highlights:
AI Lead Generation Dashboard
Scheduled Automation Management
Sales & Prospecting Analytics
Clean SaaS Dashboard UI
Modern B2B User Experience
Available for UI/UX & AI Product Design projects. 🎯