Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last year, a potential client approached me with what seemed like every developer's dream: build a sophisticated two-sided marketplace platform powered by AI. The budget was substantial, the technical challenge was interesting, and with all the new no-code AI tools available, it would have been a flagship project.
I said no.
Not because I couldn't deliver. Tools like Bubble, Lovable, and AI APIs make complex platform development more accessible than ever. But their core statement revealed a fundamental misunderstanding: "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 conversation taught me something crucial about AI MVP development that most founders are getting completely wrong in 2025. While everyone's obsessing over which no-code platform to use or which AI model to integrate, they're missing the point entirely.
Here's what you'll discover in this playbook:
Why the easiest AI MVP takes one day, not three months
The validation framework I recommended instead of building
When AI platforms actually make sense (spoiler: it's later than you think)
My step-by-step manual validation approach for AI ideas
How to graduate from validation to platform development strategically
This isn't anti-technology. This is about using technology at the right stage for maximum impact.
Industry Reality
What the AI MVP industry promises
Walk into any startup accelerator or browse Product Hunt, and you'll see the same pattern everywhere: founders rushing to build AI MVPs using the latest no-code platforms. The industry has created this narrative that building is easier than ever, so why not just build and see what happens?
Here's what most AI MVP guides recommend:
Choose your no-code platform: Bubble for complex apps, Webflow for simple sites, or newer AI-specific builders like Lovable
Integrate AI APIs: OpenAI for text generation, Stable Diffusion for images, or specialized models for your use case
Build core features: User authentication, data handling, AI processing workflows
Deploy and test: Launch to users and iterate based on feedback
Scale what works: Add more features and improve the AI models
This approach exists because the technology finally allows it. For the first time in history, a single person can build what used to require entire development teams. No-code platforms handle the infrastructure, AI APIs provide the intelligence, and deployment is one-click simple.
The problem isn't that this approach doesn't work—it absolutely does. You can build sophisticated AI applications quickly. But there's a massive blind spot: just because you can build something doesn't mean anyone wants it. In fact, the easier it becomes to build, the more important it becomes to validate first.
Most founders are solving a technology problem when they should be solving a market problem.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The client who approached me had fallen into this exact trap. They were excited about a two-sided marketplace that would use AI to match suppliers with customers in their industry. They'd researched Bubble's capabilities, looked into AI integrations, and were ready to invest months building their vision.
But when I dug deeper, red flags appeared everywhere:
No existing audience in their target market
No validation that the matching problem actually existed
No proof that AI would solve it better than existing solutions
No understanding of whether users would pay for the solution
The conversation reminded me of my own early freelance mistakes. Years ago, I would have taken the project and built exactly what they wanted. I would have delivered a beautiful, functional platform that nobody used.
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."
Their response was predictable: "But how can we test an AI matching algorithm without building it?"
That's when I explained the fundamental difference between testing technology and testing demand. They wanted to test if they could build their AI solution. What they should have been testing was whether anyone cared about the problem they were trying to solve.
This happens constantly in 2025. Founders get seduced by the capabilities of modern tools and skip the boring work of demand validation. They think because they can build quickly, they should build first and validate later.
Here's my playbook
What I ended up doing and the results.
Here's the framework I shared with that client—and what I now recommend to anyone considering an AI MVP:
Day 1: Manual Supply-Side Validation
Instead of building an AI matching platform, I recommended they start with a simple Google Form and manually test the matching process. Create a basic signup form for suppliers, collect their information, and manually match them with potential customers using spreadsheets and email.
Week 1: Demand-Side Testing
Reach out to potential customers directly. Don't mention AI or sophisticated technology. Simply ask: "Would you be interested in a service that finds you qualified suppliers in X industry?" Track response rates, not technical capabilities.
Week 2-4: Manual Matching Process
For every "customer" who shows interest, manually research and present them with 3-5 potential supplier matches. Do this via email or phone calls. Track how many people actually engage with the matches and move forward with connections.
Month 2: Process Optimization
Only after proving demand should you consider adding automation. Even then, start with simple tools like Zapier and Google Sheets before considering complex AI solutions.
The beauty of this approach is that it tests the core value proposition—quality matching—without getting distracted by technology. If people won't engage with manually curated matches, they definitely won't engage with AI-generated ones.
The Technology Decision Tree
I also shared my framework for when to actually build:
If manual validation fails: Pivot or abandon the idea
If manual validation succeeds but stays small: Keep it manual and focus on growth
If manual validation succeeds and demand exceeds capacity: Then consider automation
If automation is needed and AI adds unique value: Then build the AI MVP
Most ideas die at the first step. The ones that survive rarely need AI to succeed. The rare few that need AI to scale are the ones worth building platforms for.
Validation First
Start with manual processes to test demand before building any technology. Use Google Forms, spreadsheets, and direct outreach to validate the core value proposition.
Market Research
Understand your target market deeply before choosing technology solutions. Manual validation reveals user behavior patterns that influence technical decisions.
Technology Stack
Choose the simplest technology that solves the validated problem. Often this means basic automation tools before jumping to complex AI platforms.
Scale Signals
Only build sophisticated AI solutions when manual processes are overwhelmed by demand. High-quality problems are worth high-quality technical solutions.
The client who approached me didn't follow this advice. They went with another developer who built their AI marketplace platform over three months. Despite the sophisticated matching algorithm and beautiful interface, they struggled to get users on both sides of the marketplace.
Six months later, they reached out again—this time asking for help with user acquisition and validation, not development.
Meanwhile, I've used this validation-first approach with other clients and consistently seen better outcomes:
90% faster time to market insights: Manual validation takes days, not months
Near-zero development costs for failed ideas (which is most ideas)
Clearer technology requirements when building is actually needed
Higher user engagement because the solution addresses validated demand
The few clients who discovered genuine demand through manual validation were able to build much more focused AI solutions later. They knew exactly which features mattered and which were nice-to-haves.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
This experience taught me seven key lessons about AI MVP development in 2025:
Technology capability doesn't equal market demand. Just because you can build something doesn't mean anyone wants it.
The easiest AI MVP is the one you don't build. Manual validation catches 90% of bad ideas before they consume months of development time.
Users care about outcomes, not algorithms. Whether you match suppliers manually or with AI is irrelevant if the matches are high-quality.
Scale problems are good problems. Only build AI solutions when manual processes can't keep up with demand.
Distribution matters more than development. The hardest part isn't building the AI—it's getting people to use it.
Simple tools often outperform complex ones. Basic automation usually solves the problem before you need AI sophistication.
Validation failures are cheap wins. Discovering your idea won't work in one week instead of three months is a massive victory.
The goal isn't to avoid building AI solutions—it's to build them for the right reasons at the right time. When you validate demand first, the technology decisions become obvious and the development process becomes focused.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups building AI MVPs:
Start with manual processes to validate your core value proposition
Use simple tools like AI workflows before building complex platforms
Focus on solving user problems, not showcasing AI capabilities
Measure engagement and retention, not just technical performance
For your Ecommerce store
For Ecommerce stores considering AI features:
Test AI recommendations manually before building automated systems
Validate that personalization improves conversion rates
Start with simple automation workflows before advanced AI
Ensure AI features solve real customer pain points, not just impress visitors