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. The budget was substantial, the technical challenge was interesting, and it would have been one of my biggest projects to date.
I said no.
Here's why—and what this taught me about the real purpose of MVPs in 2025. The client came to me excited about no-code tools and AI platforms like AI automation solutions, believing they could build anything quickly and cheaply. They weren't wrong technically, but their core statement revealed the problem: "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. This conversation changed how I think about startup growth strategies and what an MVP should actually accomplish.
What you'll learn from this experience:
Why "testing market demand" shouldn't require months of development
The 1-day MVP validation framework I recommend instead
How to prove demand before building anything complex
When AI tools help vs. when they become expensive distractions
The real constraint in 2025: knowing what to build, not how to build it
Reality Check
What every founder believes about MVPs
Walk into any startup accelerator or scroll through Product Hunt, and you'll hear the same advice about launching an AI MVP quickly. The conventional wisdom sounds logical:
Build fast, iterate faster: Use no-code tools and AI to ship in weeks, not months
Start with a simple version: Launch with core features and add complexity later
Gather user feedback: Let real users guide your product development
Fail fast, pivot faster: Test assumptions quickly and cheaply
Technical execution is the bottleneck: The faster you can code, the faster you can validate
This philosophy exists because it worked brilliantly in the early 2000s when building software was genuinely hard and expensive. Back then, the constraint was technical—you needed months and significant capital just to get something basic running.
But here's where this breaks down in 2025: the constraint isn't building anymore. With AI tools, no-code platforms, and modern development frameworks, anyone can build a functional product in days or weeks. The real challenge has shifted completely.
The problem with traditional MVP advice is that it optimizes for the wrong thing. It assumes your biggest risk is technical execution, when your actual biggest risk is building something nobody wants. Most founders spend 90% of their time perfecting the product and 10% understanding their market. It should be reversed.
When everyone can build fast, speed of development stops being a competitive advantage. Distribution, audience building, and market understanding become the real differentiators.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The client who wanted the two-sided marketplace platform was smart, well-funded, and had done their homework on no-code tools. They'd researched AI development platforms, studied successful marketplace models, and even had wireframes ready.
But when I asked them basic questions, the gaps became obvious:
"Who are your first 10 customers?" - They didn't know
"Have you manually matched supply and demand?" - Never tried
"What's your customer acquisition cost?" - No idea
"How will you solve the chicken-and-egg problem?" - They hoped the platform would figure it out
This is the classic pattern I see with AI MVP projects. Founders get excited about the technology—chatbots, recommendation engines, automated workflows—but skip the fundamental business validation. They want to build an AI solution before proving there's a problem worth solving.
I've seen this same mistake across different industries. A fintech startup wanted an AI-powered investment advisor before understanding their target customers' actual decision-making process. An e-commerce client wanted personalized product recommendations before having enough transaction data to make them meaningful.
The technical capability exists to build these things quickly, but that's exactly the trap. When building is easy, it becomes the default response to every business question. "Should we validate this manually first?" becomes "Let's just build it and see what happens."
In my experience working with startups, the ones that succeed don't start with the most sophisticated technical implementation. They start with the most direct path to proving demand exists. The technology comes later, once they understand what they're actually solving.
Here's my playbook
What I ended up doing and the results.
Instead of taking their money to build a platform they might not need, I walked them through what I call the "Day 1 Validation Framework"—a systematic approach to proving demand before building anything complex.
Step 1: Create a Single-Page Validation Site (Day 1)
I recommended they create a simple landing page explaining their value proposition. Not a demo, not a prototype—just clear copy describing what they wanted to build and why it would matter. Include a signup form for early access. This takes one day maximum and costs almost nothing.
Step 2: Manual Market Matching (Week 1)
Rather than building automation, I suggested they manually connect supply and demand via email, phone calls, or even WhatsApp. If you can't make your marketplace work manually with 10 transactions, automating it won't magically fix the fundamental issues.
I've applied this approach across multiple SaaS validation projects. One client wanted to build an AI-powered content calendar. Instead of developing the AI first, we started with a Google Sheets template and manual curation. Within two weeks, we had 50 customers paying for the manual version. Only then did we consider automation.
Step 3: Distribution Before Development (Week 2-4)
The next phase focuses entirely on audience building and distribution. Start creating content around your problem space, engage in relevant communities, and build relationships with potential customers. Most founders skip this step and wonder why nobody uses their perfectly built product.
Step 4: Progressive Automation (Month 2+)
Only after proving manual demand do you start building automated solutions. But here's the key: you build automation for processes you've already validated manually. You're not guessing what features matter—you know because you've done it by hand.
This approach completely flips the traditional MVP model. Instead of building first and finding customers later, you find customers first and build exactly what they need.
Problem First
Focus on validating the core business problem before any technical implementation. If you can't solve it manually, automation won't save you.
Distribution Early
Start building your audience and distribution channels before your product. The best product in the world fails without people who know it exists.
Manual Testing
Run your business model manually for at least 10 transactions before automating anything. This reveals the real friction points and value drivers.
AI as Enhancement
Use AI to enhance proven processes, not to replace unvalidated business models. AI amplifies what works—it doesn't create demand where none exists.
The results of this approach speak for themselves, though not always in the way founders expect. The two-sided marketplace client initially felt disappointed—they wanted to build something, not run experiments with spreadsheets.
But three months later, they contacted me with a completely different perspective. Their manual testing revealed that their original marketplace idea had fundamental flaws. The supply side was much harder to onboard than anticipated, and the demand side had different needs than they'd assumed.
However, through their manual testing process, they discovered a adjacent opportunity they hadn't considered. Instead of a two-sided marketplace, they found demand for a simple SaaS tool that solved one specific problem for their target customers. This required 1/10th of the development effort but generated revenue faster.
I've seen similar outcomes across multiple projects. A client who wanted to build an AI writing assistant discovered through manual testing that their customers actually needed editing help, not generation help. Another startup planning a complex recommendation engine found that simple category filtering solved 80% of their users' needs.
The pattern is consistent: manual validation either saves you from building the wrong thing or reveals a simpler path to the same outcome. Either way, you end up with a stronger business foundation than if you'd started with complex development.
The time investment tells the real story: validation takes weeks, building takes months. When you validate first, you either save months of wasted development or ensure those months are spent building something people actually want.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After applying this framework across dozens of client projects, several key lessons emerged that challenge conventional MVP wisdom.
Lesson 1: Speed of validation beats speed of development. The fastest path to market isn't the fastest code—it's the fastest path to proving demand exists. I've seen too many technically impressive products fail because nobody wanted them.
Lesson 2: Manual processes reveal real customer needs. When you automate too early, you automate your assumptions. Manual testing forces you to understand what customers actually value versus what you think they should value.
Lesson 3: Distribution is harder than development. Building features is straightforward; finding people who care about those features is the real challenge. Start with distribution and work backward to features.
Lesson 4: AI amplifies existing processes, not broken ones. The most successful AI implementations I've seen enhance workflows that already work manually. Using AI to fix fundamental business model problems rarely succeeds.
Lesson 5: Customer development trumps product development. Your first hire shouldn't be a developer—it should be someone focused entirely on understanding and acquiring customers. Product decisions become obvious when you deeply understand your market.
What I'd do differently: Push clients harder on manual validation before any development. Even when they're eager to build, the discipline of proving demand manually always reveals insights that save time and money later.
When this approach works best: B2B SaaS, marketplaces, service businesses, and any product where customer behavior needs to be understood before automation. When it doesn't: Consumer mobile apps where the experience itself is the differentiator, or highly technical products where the implementation is genuinely novel.
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 landing page + manual onboarding before building any product features
Run your entire customer success process manually for first 10-20 customers
Build audience through content before building product through code
Use AI for optimization, not for core business model validation
For your Ecommerce store
For ecommerce businesses looking to integrate AI:
Test personalization manually through customer interviews before building recommendation engines
Validate inventory needs through pre-orders before automating supply chain
Start with simple chatbots for FAQs before complex AI customer service
Focus on conversion optimization before AI-powered features