Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, a potential client approached me with what seemed like a dream project: build a comprehensive two-sided marketplace platform with a substantial budget. The technical challenge was exciting, and it would have been one of my biggest projects to date.
I said no.
Instead, I told them something that initially shocked them: "If you're truly testing market demand, your MVP should take one day to build—not three months." Even with AI and no-code tools, building a functional platform takes significant time. But here's what most founders miss: your first MVP shouldn't be a product at all.
This experience taught me something crucial about the current state of AI tools and drag-and-drop builders. While everyone's rushing to build complex platforms, they're missing the fundamental question: are we solving the right problem?
Here's what you'll learn from my contrarian approach:
Why I recommend workflow automation over platform building for most startups
The one-day MVP framework that validates ideas before you code anything
How AI workflow builders solve real problems faster than custom platforms
When drag-and-drop builders make sense (and when they don't)
My step-by-step validation process that saves months of development time
Industry Reality
What every startup founder has been told about building platforms
The startup world is obsessed with platform thinking. Every accelerator, every tech blog, every successful founder interview seems to push the same narrative: build a platform, create network effects, scale infinitely.
Here's what the conventional wisdom tells you about drag-and-drop AI builders and platforms:
Platforms create defensible moats - The more users you have on both sides, the stronger your position
AI democratizes development - Anyone can build complex software with the right no-code tools
Speed to market is everything - Get your MVP out fast and iterate based on feedback
Two-sided marketplaces are goldmines - Connect supply and demand, take a cut, profit
Technology solves adoption - Build it well enough and users will come
This thinking exists because survivor bias is everywhere. We hear about the Ubers, Airbnbs, and Shopifys of the world, but we don't hear about the thousands of failed marketplace attempts. What's missing from this narrative is a crucial insight: most successful platforms didn't start as platforms.
The problem with this advice? It conflates the end state with the starting strategy. Yes, platforms can be incredibly valuable once they achieve critical mass. But getting to that critical mass is where most founders fail, especially when they treat technology as the solution to what is fundamentally a distribution and validation problem.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When this client came to me with their marketplace idea, they had all the classic symptoms: excitement about the technology, confidence in the concept, and zero proof of demand. They'd heard about AI builders and were convinced they could validate their idea by building a "quick" platform.
"We want to see if our idea works," they told me. Red flag number one.
They had no existing audience, no validated customer base, no proof of demand—just an idea and enthusiasm. The plan was to use modern AI drag-and-drop tools to build a two-sided marketplace connecting service providers with customers in a specific niche.
Here's where it gets interesting: they weren't wrong about the technology. Tools like Bubble, Webflow, and various AI platforms can absolutely build functional marketplaces quickly. But they were completely wrong about what they needed to validate.
I've seen this pattern repeatedly in my consulting work. Founders get excited about AI automation capabilities and think building the platform is the hard part. In reality, the platform is usually the easy part. The hard part is answering three fundamental questions:
Do people actually want what you're building?
Will they pay for it?
Can you reach them consistently?
My client was trying to answer these questions by building technology. That's like trying to test if people want pizza by opening a restaurant. The investment is too high and the feedback loop is too slow.
This is when I realized something important: most founders confuse "building" with "validating." They think because they can build something quickly with AI tools, they should build something to test their hypothesis. But validation and building are two completely different activities that require different approaches.
Here's my playbook
What I ended up doing and the results.
Instead of building their platform, I proposed something radical: test the marketplace hypothesis manually. This became my framework for what I call "pre-platform validation."
Day 1: Create a Simple Landing Page
Not a platform—a single page explaining the value proposition. I had them create a basic Notion doc outlining what they wanted to build and why it would be valuable. Total time investment: 2 hours.
Week 1: Manual Outreach to Both Sides
Instead of building supply and demand matching algorithms, they manually reached out to potential service providers and customers. The goal wasn't to scale—it was to understand if the pain points they assumed actually existed.
This is where distribution strategy becomes crucial. You can't test a marketplace without testing your ability to reach both sides of it.
Week 2-4: Manual Matchmaking
When they found interested parties on both sides, they manually matched them via email and WhatsApp. No algorithms, no automation, no platform. Just pure human coordination to see if transactions would actually happen.
Only After Proving Demand: Consider Automation
Here's where AI workflow builders actually become valuable. Once you've proven that people want what you're offering and will pay for it, then you can think about scaling with technology.
The key insight: your MVP should be your marketing and sales process, not your product. Distribution and validation come before development.
This approach completely flips the traditional "build first, validate later" mentality. Instead of asking "How do we build this?" you start by asking "How do we prove people want this?"
When I work with SaaS startups now, I always recommend this validation-first approach before any platform building. The constraint isn't building—it's knowing what to build and for whom.
The beautiful thing about this framework is that it works whether you're planning to use drag-and-drop builders, hire developers, or build everything yourself. The validation principles remain the same regardless of your technical approach.
Validation Framework
Test market demand before building anything - create simple landing pages and manual processes to prove people want what you're offering
Manual Matchmaking
Handle initial transactions manually via email/WhatsApp to understand real user behavior and pain points before automating
Distribution First
Focus on proving you can reach both sides of your marketplace consistently before building the platform technology
Automation Last
Only invest in AI builders and automation after validating demand through manual processes and proven distribution channels
The results of this approach were eye-opening. Within 30 days, my client had validated their core assumptions without writing a single line of code or spending money on development.
What They Discovered:
The pain point was real, but not urgent enough for most potential customers
Service providers were interested but wanted different features than originally planned
The pricing model they'd assumed wouldn't work needed significant adjustment
Customer acquisition was much harder than expected on one side of the marketplace
Most importantly, they learned all of this without the sunk cost of a built platform. They could pivot their approach based on real user feedback rather than trying to force users to adapt to their pre-built solution.
This validation process saved them an estimated 3-6 months of development time and tens of thousands in development costs. More importantly, it gave them a much clearer understanding of what they'd need to build if they decided to proceed.
The twist? After this validation process, they decided to pivot to a completely different approach that better matched what they'd learned about their market. Something that would have been impossible if they'd already invested in building the original platform.
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 when and how to use drag-and-drop AI builders effectively:
Validation first, automation second - Never build technology to test a hypothesis. Use technology to scale proven processes.
Manual processes reveal automation opportunities - You can't automate something you don't understand. Manual work shows you exactly what needs to be automated.
Distribution is harder than development - AI tools make building easier, but they don't solve your customer acquisition challenges.
Sunk cost bias kills startups - The more you invest in building before validating, the harder it becomes to pivot when you learn you're wrong.
Speed to learning beats speed to market - Getting feedback fast matters more than shipping fast.
Technology amplifies clarity, not confusion - If you're unclear about your value proposition, AI builders won't help. They'll just help you build the wrong thing faster.
Platforms require network effects - You need proven ability to attract both sides before the platform makes sense.
The broader lesson: in the age of AI and no-code tools, the constraint isn't technical capability—it's knowing what to build and for whom. Drag-and-drop builders are incredibly powerful for scaling validated ideas, but they're terrible for testing unvalidated assumptions.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups specifically:
Start with manual onboarding and support processes before automating
Use workflow automation to scale customer success, not to replace customer discovery
Focus on one side of your platform first - perfect serving one user type before adding complexity
For your Ecommerce store
For ecommerce stores:
Test product-market fit with manual fulfillment before automating supply chain
Use AI builders for customer service automation only after understanding common support issues
Validate demand through pre-orders or waitlists before building inventory management platforms