Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last year, a potential client approached me with an exciting opportunity: build a two-sided marketplace platform powered by AI. The budget was substantial, the technical challenge was interesting, and it would have been one of my biggest projects to date.
I said no.
Not because I couldn't build it—with tools like Bubble, Lovable, and the latest AI integrations, creating complex platforms has never been easier. I turned it down because they were asking the wrong question entirely.
The client's core statement revealed everything: "We want to see if our idea is worth pursuing." They had no existing audience, no validated customer base, no proof of demand—just an idea and enthusiasm for the latest no-code AI tools.
Here's what you'll learn from my experience with this decision:
Why AI-powered MVPs often solve the wrong problem
The real purpose of validation in 2025 (hint: it's not about building faster)
My framework for distinguishing between genuine testing and expensive assumptions
What I recommended instead—and why it worked
When no-code AI tools actually make sense for SaaS startups
The no-code AI revolution has made building incredibly accessible, but it's also made it easier to build the wrong thing faster than ever before.
Market Reality
What the industry promises about no-code AI MVPs
Walk into any startup accelerator or browse through Product Hunt, and you'll hear the same excitement about no-code AI platforms. The narrative is compelling and everywhere:
"Build your MVP in days, not months." Tools like Bubble, Webflow, and Lovable promise rapid prototyping with AI assistance. The technical barriers have never been lower.
"Test your idea without technical debt." No-code advocates argue you can validate concepts without expensive development cycles or technical co-founders.
"AI makes everything possible." From ChatGPT integrations to automated workflows, the promise is that artificial intelligence can handle the complex logic your platform needs.
"Fail fast, iterate faster." The lean startup methodology combined with no-code tools supposedly creates the perfect environment for rapid experimentation.
"Democratized development." Anyone with an idea can now build and launch without coding knowledge, leveling the playing field for non-technical founders.
This conventional wisdom exists because there's truth in it. No-code tools have genuinely revolutionized rapid prototyping. AI integration has made sophisticated features accessible to non-developers. The barrier to building something has never been lower.
But here's where this thinking falls short: it confuses building capability with market validation. The ability to build quickly doesn't address whether you should build at all. In fact, the ease of building can become a distraction from the harder work of understanding your market.
The real constraint in 2025 isn't technical—it's knowing what to build and for whom. And that's where most no-code AI MVP strategies completely miss the point.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When this client contacted me, I was genuinely excited. They'd done their homework on the technical side—they knew about Bubble's capabilities, understood AI integration possibilities, and had researched the no-code landscape thoroughly. The budget was substantial enough to build something impressive.
Their pitch was textbook startup material: a two-sided marketplace that would "revolutionize" how their industry connected supply and demand. They wanted to use AI for matching algorithms, automated onboarding, and intelligent recommendations. On paper, it sounded like exactly the kind of innovative project that no-code AI tools were designed for.
But as we discussed their business model deeper, red flags started appearing:
Zero existing audience. They hadn't built any following in their target market. No email list, no social media presence, no network of potential early adopters.
No customer interviews. When I asked about their understanding of customer pain points, they referenced "industry research" and assumptions rather than direct conversations with potential users.
Platform-first thinking. They were convinced they needed to build both sides of the marketplace simultaneously before they could test anything.
Technology-driven validation. Their entire testing strategy revolved around "if we build it with the right features, they will come."
This is when I realized we were dealing with a fundamental misunderstanding about what MVPs are supposed to accomplish. They weren't looking to validate demand—they were looking to validate their ability to build their predetermined solution.
The more I understood their situation, the more convinced I became that spending months building a complex platform would be the worst possible use of their time and money, regardless of how quickly no-code AI tools could help us do it.
Here's my playbook
What I ended up doing and the results.
Instead of accepting the project, I shared my framework for true market validation—what I call "marketing before building." This approach has saved multiple clients from expensive mistakes and led to much stronger product-market fit when they eventually did build.
Step 1: Manual Validation Without Product
I recommended they start with a simple landing page explaining their value proposition and begin manually connecting potential users. No AI, no complex workflows—just direct outreach and manual matching via email or phone calls.
"If you can't manually facilitate 10 successful connections between your target users," I told them, "then automating that process won't solve your problem."
Step 2: Build Audience Before Platform
Rather than building product features, focus entirely on audience building. Create content that attracts both sides of your marketplace. Build email lists. Start conversations. The goal: prove people care about this problem enough to give you their attention.
Step 3: Validate Willingness to Pay
Before writing a single line of code (or configuring any no-code workflows), prove people will actually pay for your solution. This might mean pre-selling a manual service, taking deposits for early access, or getting letters of intent from enterprise customers.
Step 4: Start Small and Manual
When you do start building, begin with the smallest possible automation. Maybe that's a simple form that triggers email notifications, not a complex AI matching algorithm. Prove the core loop works before adding sophistication.
Step 5: Technology as Scaling Solution
Only when you've manually proven demand and validated the core business model should you invest in sophisticated no-code AI tools. At this point, technology becomes about scaling what already works, not discovering whether it works.
This framework challenged everything they thought they knew about MVP development. But it addressed the real risk: building something nobody wants, faster than ever before.
Manual First
Start with human-powered validation before any automation
Distribution Focus
Build audience and prove demand before building features
Scaling Technology
Use no-code AI to automate what you've manually proven works
Risk Mitigation
Avoid the expensive mistake of building the wrong thing quickly
The outcome validated my approach completely. Instead of spending months and significant budget building a complex platform, they implemented my validation framework:
Week 1-2: Created a simple landing page and started manual outreach to both sides of their intended marketplace. Within two weeks, they'd facilitated their first successful connection entirely through email and phone calls.
Month 1: Built email lists for both user segments and started a newsletter sharing industry insights. They were learning more about their market in weeks than months of feature development would have taught them.
Month 2: Launched a manual "concierge" service, personally matching users while charging a fee. This proved willingness to pay and helped them understand the real complexity of their matching algorithm.
Month 3: With proven demand and paying customers, they started building targeted automation tools—but only for the parts of their process that were becoming genuine bottlenecks.
Six months later, they had a sustainable business with validated product-market fit. When they eventually did use no-code AI tools, it was to scale proven demand rather than test unvalidated assumptions.
The most important outcome? They avoided what I call "the beautiful failure"—a perfectly executed technical solution to a problem nobody actually had.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
This experience taught me several crucial lessons about the intersection of no-code tools, AI capabilities, and genuine market validation:
Technology amplifies strategy, good or bad. No-code AI tools don't make bad ideas better—they just help you execute bad ideas faster and more expensively.
The constraint isn't building anymore. In 2025, the bottleneck for most startups isn't technical capability—it's market understanding and distribution.
Manual processes reveal hidden complexity. What seems like a simple matching algorithm becomes incredibly nuanced when you try to do it manually for real customers.
Audience building beats feature building. Time spent building an audience always delivers more insight than time spent building features without an audience.
Validation requires real commitment. True market validation means getting people to spend time, money, or attention—not just click "yes" on a survey.
AI works best for scaling, not discovering. Artificial intelligence excels at optimizing known processes, not figuring out what those processes should be.
No-code success requires more discipline, not less. The ease of building makes it even more important to resist the urge to build before validating.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups considering no-code AI MVPs:
Start with landing pages and manual processes before building complex workflows
Focus on user acquisition strategies that don't require your product to exist
Use AI for content generation and customer support, not core product logic initially
Validate willingness to pay before building subscription infrastructure
For your Ecommerce store
For ecommerce businesses exploring no-code AI solutions:
Test product demand through manual sales before building automated systems
Use proven platforms like Shopify rather than custom no-code solutions
Implement AI for optimization and personalization after establishing baseline performance
Focus on distribution and marketing before complex product recommendation engines