Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last year, a potential client approached me with what seemed like a dream project: building a comprehensive two-sided marketplace platform with AI features. The budget was substantial, the technical challenge was exciting, and it would have been one of my biggest freelance projects to date.
I said no.
Not because the project wasn't appealing, but because their approach was fundamentally flawed. They wanted to "test if their idea worked" by building a complex platform that would take months to develop. Meanwhile, AI tools like Bubble were being promoted as the solution to "build anything quickly." But here's what most founders miss: if you're truly testing market demand, your MVP should take one day to build, not three months.
Through working with multiple clients on MVP validation, I've learned that the biggest mistake isn't choosing the wrong platform—it's building the wrong thing entirely. Most businesses treat MVPs like mini-versions of their final product when they should be treating them as validation experiments.
Here's what you'll learn from my experience:
Why I turned down a lucrative platform project and what I recommended instead
The real purpose of MVPs in 2025 (it's not what you think)
My framework for validating ideas before building anything
When AI tools like Bubble actually make sense for MVP development
A step-by-step approach that saves months of development time
This isn't about AI tools or no-code platforms—it's about understanding what an MVP should actually accomplish.
Industry Reality
What the startup world preaches about MVPs
Walk into any startup accelerator or read any entrepreneurship blog, and you'll hear the same advice about MVPs: "Build fast, fail fast, iterate fast." The tech industry has created an entire ecosystem around this philosophy, with platforms like Bubble, Webflow, and various AI tools promising to help you "build your MVP in days, not months."
The conventional wisdom follows a predictable pattern:
Start with a no-code platform - Use tools like Bubble to rapidly prototype your idea
Add AI features - Integrate ChatGPT APIs or other AI services to make your product "intelligent"
Launch quickly - Get your MVP in front of users within weeks
Collect feedback - Use user behavior to iterate and improve
Scale up - Move to custom development once you've proven product-market fit
This approach exists because the startup ecosystem has confused "building fast" with "validating fast." VCs and accelerators see demo days filled with functional prototypes, creating the illusion that technical execution equals market validation. No-code platforms capitalize on this by positioning themselves as the solution to "expensive development cycles."
The problem? Most founders using this approach are optimizing for the wrong metric. They're measuring success by how quickly they can build features rather than how quickly they can validate demand. I've seen countless startups spend months perfecting their Bubble app only to discover that nobody actually wants what they've built.
The real issue isn't the tools—Bubble and AI integrations can be powerful when used correctly. The issue is treating MVP development like product development when it should be treated like marketing and sales development.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The client who approached me had everything mapped out. They'd done market research, identified their target users, and even designed user flows for their two-sided marketplace. Their plan was to use Bubble with AI integrations to create a functional platform where buyers and sellers could connect, complete with automated matching algorithms and payment processing.
"We want to see if our idea is worth pursuing," they told me during our initial call. They had no existing audience, no validated customer base, and no proof of demand—just enthusiasm and a solid budget.
As they walked me through their vision, I realized they were making the classic mistake I'd seen with multiple clients before. They were confusing "testing an idea" with "building a product." Even with Bubble and AI tools making development faster, we were still talking about months of work to create something that looked and felt like a real platform.
But here's what struck me: they had never manually connected a single buyer with a single seller. They wanted to build automated systems before proving that manual systems could work. They wanted to create algorithms before understanding what criteria actually mattered to their users.
I started asking different questions: "Have you tried manually matching buyers and sellers via email? Have you posted in relevant communities to see if people respond to your value proposition? Have you created a simple landing page to gauge interest?" The answers were all no.
That's when I realized the fundamental problem with their approach—and with most MVP strategies I'd encountered. They were treating technology as the validation method instead of the scaling method.
Here's my playbook
What I ended up doing and the results.
Instead of accepting the project, I shared a completely different framework with them. I call it the "Manual-First MVP" approach, and it's designed around a simple principle: if you can't make it work manually, automation won't save you.
Here's the step-by-step process I recommended:
Day 1: Create Your Value Proposition Test
I told them to create a simple landing page or Notion doc explaining their value proposition. Not a functional platform—just a clear explanation of what they promised to do for both buyers and sellers. This takes one day, not months.
Week 1: Manual Outreach and Discovery
Instead of building algorithms, start with manual outreach to potential users on both sides of the marketplace. Post in relevant communities, reach out directly, and see if people actually respond to your core proposition. The goal isn't to make sales—it's to validate that the problem you're solving actually exists.
Week 2-4: Manual Matching Process
When you find interested buyers and sellers, connect them manually via email, WhatsApp, or whatever communication method works. Track every interaction, note what works, what doesn't, and what criteria actually matter to both sides. This manual process becomes your algorithm blueprint.
Month 2: Document and Systematize
Only after proving that manual matching works should you consider building automation. By this point, you'll know exactly what features matter, what user flows actually make sense, and whether there's enough demand to justify development time.
The beauty of this approach? Your MVP becomes your marketing and sales process, not your product. You're testing distribution and demand validation—the things that actually determine startup success—before you worry about technical implementation.
If manual matching fails, you've saved months of development time. If it succeeds, you have a proven business model ready for automation. That's when tools like Bubble and AI integrations become powerful—they're scaling solutions, not validation solutions.
Validation First
Prove demand exists before building anything. Manual processes reveal what users actually want versus what you think they want.
Real Problem Discovery
Manual interactions expose the actual pain points and workflows that matter to users, not theoretical ones.
Distribution Testing
Learn how to find and convert customers before automating the process. Your MVP should test your go-to-market strategy.
Foundation Building
Document manual processes to create a blueprint for automation. Your human workflows become your algorithm specifications.
The client initially pushed back. "But we want to test if our AI matching algorithms work," they argued. I explained that algorithms are optimization problems—you need to know what you're optimizing for first.
They decided to try the manual approach. Within two weeks, they discovered something crucial: the problem they thought they were solving wasn't the real problem. The sellers didn't need better matching algorithms—they needed better lead quality. The buyers weren't struggling to find sellers—they were struggling to evaluate which sellers were legitimate.
This insight completely changed their product direction. Instead of building a complex marketplace with AI matching, they pivoted to a simple vetting service that manually verified sellers and provided quality scores. This was something they could do immediately, without any platform development.
Within a month, they had paying customers. Within three months, they had consistent revenue. They eventually built a platform, but it looked nothing like their original vision—and it was infinitely more valuable because it solved the actual problem they'd discovered through manual validation.
The lesson? The best MVPs don't validate your product idea—they validate your understanding of the problem. Technology should amplify validated processes, not create unvalidated ones.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After implementing this approach across multiple client projects, I've identified seven critical lessons about MVP development in 2025:
Manual validation beats technical validation - If you can't make it work with humans, algorithms won't fix it
Distribution trumps features - Most startups fail because they can't find customers, not because their product is bad
Real user behavior differs from theoretical user behavior - Manual processes reveal actual workflows and pain points
AI tools are amplifiers, not validators - Use them to scale proven processes, not to test unproven concepts
Speed to learning matters more than speed to building - Validate assumptions faster, build slower
Your MVP should test your business model, not your technical skills - Focus on proving demand before proving capability
The best platform is often no platform - Start with existing tools (email, spreadsheets, communication apps) before building custom solutions
The biggest mistake I see founders make is treating MVPs like miniature versions of their final product. An MVP should be an experiment designed to test your riskiest assumptions about user behavior and market demand. Everything else is premature optimization.
When you do eventually need to build technology, that's when platforms like Bubble become incredibly valuable. But by then, you're not guessing what to build—you're systematizing what you've already proven works.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups building AI-powered MVPs:
Start with manual customer success processes before automating anything
Test your onboarding flow manually with individual users
Validate that your AI features solve real problems, not just technical challenges
Focus on proving retention before building acquisition features
For your Ecommerce store
For ecommerce stores considering AI-powered features:
Manually curate product recommendations before building recommendation engines
Test personalized experiences through email before building on-site personalization
Validate demand for AI-powered features through customer interviews
Prove that automation improves conversion, not just convenience