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 with AI capabilities. The budget was substantial, the technical challenge was interesting, and with all the new AI tools and no-code platforms available, it would have been one of my biggest projects to date.
I said no.
Not because I couldn't deliver it. Tools like Bubble, Lovable, and AI APIs make complex platform development more accessible than ever. But their core statement revealed the problem: "We want to test if our AI idea works."
They had no existing audience, no validated customer base, no proof of demand. Just an idea and enthusiasm for AI technology. This is the trap I see everywhere in 2025: founders think AI tools make validation faster, when they actually make expensive assumptions faster.
This conversation taught me everything about building MVPs for AI-driven applications. While everyone's obsessing over which AI model to use or which no-code platform to choose, they're missing the fundamental point entirely.
Here's what you'll discover in this playbook:
Why the best AI MVP takes one day to build, not three months
The validation framework that saved my client thousands in development costs
When AI platforms actually make sense (spoiler: it's later than you think)
My step-by-step manual validation approach for AI concepts
How to graduate from validation to platform development strategically
This approach has saved multiple clients from building expensive SaaS solutions nobody wanted.
Reality Check
What Nobody Tells You About AI MVPs
Walk into any startup accelerator or browse ProductHunt, and you'll see the same advice everywhere: "Build fast, iterate faster." The AI MVP guidance is particularly seductive:
Choose your AI stack - OpenAI, Claude, or local models
Pick a no-code platform - Bubble for complex apps, Webflow for simple ones
Integrate AI APIs - Natural language processing, computer vision, whatever fits
Deploy and test - Get users, measure engagement, iterate
Scale based on data - Add features, improve models, grow
This approach exists because the technology finally allows it. AI APIs are accessible, no-code platforms are powerful, and deployment is straightforward. The AI development ecosystem has never been more founder-friendly.
Accelerators love this approach because it looks like progress. VCs love it because it demonstrates technical execution. Founders love it because it feels like building the future.
But here's where this conventional wisdom falls short: It assumes your biggest risk is execution, when your biggest risk is actually building something nobody wants. All those accessible AI tools make it easier than ever to build the wrong thing beautifully.
Most AI MVPs fail not because the technology doesn't work, but because founders skip the crucial step of validating demand before touching any code.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When this potential client came to me, they had everything figured out - except the most important part. They wanted to build a two-sided marketplace that would use AI to match supply and demand more effectively than existing solutions.
Their pitch was compelling: "We want to see if our AI idea is worth pursuing." They'd researched the tech stack, identified the right no-code tools, and had budget allocated. From a project management perspective, it seemed straightforward.
But as I dug deeper into their assumptions, red flags started appearing. They had no existing audience in either side of their marketplace. No validated customer segments. No proof that their "more effective matching" actually solved a problem people were willing to pay for.
This is when I realized they weren't trying to test market demand - they were trying to test if they could build their vision. These are completely different questions, and the second one is far less important.
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."
Their first reaction was confusion. How could you test an AI-driven marketplace in one day? Where's the machine learning? Where's the sophisticated matching algorithm? Where's the beautiful interface?
That's when I explained the fundamental difference between testing technology and testing market demand. Yes, AI tools and no-code platforms make building faster. But they also make expensive assumptions faster. If you're wrong about what people want, you'll be wrong efficiently.
The client went elsewhere initially, convinced they needed to build to validate. Six months later, they came back after spending their budget on a platform that generated minimal user interest. They were ready to try the one-day approach.
Here's my playbook
What I ended up doing and the results.
Here's the framework I recommended instead of building their AI platform immediately:
Day 1: Manual MVP Creation
Instead of building an AI matching system, I had them create a simple landing page explaining their value proposition. Not their technology - their value. "Better matches between X and Y" became "Get connected to the right Y for your specific X needs."
The page included:
Clear problem statement
Promise of solution (without mentioning AI)
Simple signup form for both sides
"Coming soon" messaging
Week 1: Manual Outreach
Rather than waiting for signups, they started direct outreach to potential users on both sides. This wasn't scalable, but it was educational. They learned more about their target market in one week of conversations than months of research could provide.
Key activities:
LinkedIn outreach to potential supply-side users
Email campaigns to potential demand-side users
Direct conversations about pain points and current solutions
Week 2-4: Manual Matching
When they got interest from both sides, they did the "matching" manually via email and spreadsheets. No AI, no algorithms, just human intelligence understanding what each side needed and making introductions.
This phase revealed:
What information was actually needed for good matches
How complex the matching logic needed to be
Whether people valued the matching service enough to pay for it
What the real friction points were in their market
Month 2: Validation-Driven Development
Only after proving demand manually did we start building technology. But now we knew exactly what to build and for whom.
The development priorities became:
Simple matching interface - Based on what worked manually
Basic automation - No AI yet, just workflow optimization
Payment processing - Because we'd validated willingness to pay
User feedback loops - To improve matching over time
The AI component came later, in Month 3, when we had enough data and validated demand to justify the complexity. By then, we knew exactly what the AI needed to optimize.
Validation First
Test demand before building any technology. A simple landing page and manual processes reveal more than sophisticated platforms.
Manual Matching
Do the core function manually first. This reveals what automation actually needs to solve and validates the value proposition.
Progressive Complexity
Start simple, add sophistication only when validated. AI should enhance a proven model, not create an unproven one.
Real User Learning
Direct user conversations teach you what to build. Assumptions and research can't replace actual customer development.
The results of this approach compared to traditional "build-first" AI MVPs were striking:
Time to Market Validation: 30 days vs. 3+ months for full platform development. We knew whether the core concept had demand before investing in complex technology.
Development Cost Savings: The manual validation phase cost under $500 (landing page, email tools, time). Compare this to $15,000+ for a no-code platform with AI integrations that might validate nothing.
User Learning Quality: Manual processes forced direct customer interaction, revealing insights that automated systems would have hidden. We learned what users actually valued vs. what they said they valued.
Technical Precision: When we finally built the AI components, we knew exactly what problem to solve. No feature bloat, no speculative functionality - just targeted automation of validated manual processes.
The client's second attempt (following this framework) generated paying users within 6 weeks, compared to their first attempt which struggled to find product-market fit after 6 months of development.
Most importantly, they avoided the common growth trap of having great technology solving the wrong problem.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
This experience taught me seven crucial lessons about building MVPs for AI-driven applications:
Technology is never your biggest risk - In 2025, you can build almost anything. The challenge is knowing what to build.
Manual processes reveal business logic - Doing things manually first shows you exactly what needs automation and what doesn't.
AI should enhance, not create - The most successful AI applications improve existing workflows, they don't invent new ones.
Validation isn't about technology - Users don't care how smart your AI is if it doesn't solve their problem better than current solutions.
No-code platforms can be expensive experiments - Just because you can build quickly doesn't mean you should build first.
Customer development beats customer research - Direct conversations with potential users teach you more than any survey or focus group.
Progressive complexity reduces risk - Start simple, add sophistication only when justified by real user needs.
The biggest mistake I see founders make is confusing "easy to build" with "should be built." AI tools and no-code platforms have solved the technical challenges, but they can't solve the market validation challenges for you.
Your MVP's job isn't to showcase your technical capabilities - it's to prove people want what you're offering.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups building AI-driven applications:
Start with manual processes to validate core workflows
Focus on user problem validation before AI implementation
Use simple tools (landing pages, email) for initial testing
Graduate to no-code platforms only after manual validation
Add AI when you have data and proven demand to justify complexity
For your Ecommerce store
For ecommerce companies integrating AI features:
Test recommendation logic manually with customer data first
Validate personalization concepts through email campaigns
Use simple A/B tests before building complex AI systems
Focus on enhancing existing customer journeys, not creating new ones
Measure business impact, not technical sophistication