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 features. 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 deliver it, but because they were asking the wrong question entirely. They wanted to build an AI platform to "test if their idea works" – but had no existing audience, no validated customer base, and no proof of demand. Just enthusiasm and a hefty budget.
This experience taught me something crucial about AI prototyping in 2025: the constraint isn't building anymore – it's knowing what to build and for whom. With tools like Bubble making complex development accessible, the real challenge has shifted from technical capability to market validation.
Here's what you'll learn from my contrarian approach to AI prototype development:
Why I recommend manual validation before any AI development
How to build meaningful AI prototypes that solve real problems
When no-code platforms like Bubble make sense for AI projects
My framework for avoiding expensive AI prototype failures
Real examples of AI features that actually drive business value
Industry Reality
What everyone tells you about AI prototyping
The current AI prototyping advice follows a predictable pattern. Every startup accelerator, tech blog, and consultant tells you the same thing:
"Build fast, test early, iterate quickly." The standard playbook suggests choosing a no-code platform, adding some AI APIs, launching an MVP, and seeing what sticks.
Here are the five most common recommendations you'll hear:
Start with AI-first features – Build intelligence into your core product from day one
Use no-code for speed – Platforms like Bubble let you prototype without developers
Integrate popular AI APIs – Leverage ChatGPT, Claude, or other established models
Launch and learn – Get something functional in front of users quickly
Focus on user feedback – Let the market guide your AI feature development
This advice exists because it feels logical and matches Silicon Valley mythology about rapid iteration. The tools are accessible, AI APIs are powerful, and no-code platforms have democratized development.
But here's where this conventional wisdom falls apart: it assumes your AI features need to exist at all. Most founders skip the fundamental question of whether AI actually solves a validated problem for real users willing to pay for a solution.
The result? Hundreds of AI prototypes that nobody wants, burning through budgets on sophisticated solutions to problems that don't exist. The technology works perfectly – but it's solving the wrong problems for the wrong people.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The client who approached me with the AI marketplace idea was excited about the no-code revolution. They'd heard that tools like Bubble and new AI APIs could build anything quickly and cheaply. Technically, they weren't wrong – you can build sophisticated platforms with these tools.
But their core statement revealed the fundamental 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, enthusiasm, and a budget. Sound familiar? This is exactly the trap most AI prototype projects fall into.
When I dug deeper into their "validation" process, here's what I found:
They'd surveyed friends and family (who were polite)
They'd done competitor research (but found no direct competitors)
They'd created user personas (based on assumptions)
They'd never actually spoken to potential customers
My first instinct was to take the project. The technical scope was interesting, the budget was good, and building an AI-powered marketplace on Bubble would have been a solid portfolio piece.
But then I remembered every similar project I'd seen fail. Beautiful prototypes that nobody used. Sophisticated AI features that solved problems nobody had. Founders who spent months building before discovering their market didn't exist.
That's when I realized: if you're truly testing market demand, your MVP should take one day to build – not three months.
Here's my playbook
What I ended up doing and the results.
Instead of jumping into Bubble development, I told them something that initially shocked them: their first MVP shouldn't be a product at all. Here's the framework I recommended, and now use for all AI prototype projects:
Phase 1: Manual Demand Validation (Week 1)
Day 1: Create a simple landing page explaining the value proposition. No fancy design, no AI features – just a clear description of what problem you're solving and for whom.
Week 1: Start manual outreach to potential users on both sides of the marketplace. This means actually talking to people, not sending surveys. Real conversations about real problems.
Phase 2: Human-Powered MVP (Weeks 2-4)
Instead of building AI algorithms, manually match supply and demand via email or WhatsApp. This approach reveals the actual workflow, pain points, and value exchanges that matter to users.
I learned this approach from observing successful marketplaces like Airbnb, which started with manual processes before automating. The key insight: your MVP should be your marketing and sales process, not your product.
Phase 3: Smart Automation (Month 2+)
Only after proving demand do you consider building automation. And here's where Bubble becomes valuable – not for initial validation, but for scaling proven processes.
When you do start building, focus AI features on the specific friction points you discovered during manual operations. This ensures every AI feature solves a real, validated problem.
The Bubble Implementation Strategy
When manual validation succeeds, Bubble becomes powerful for AI prototypes because:
You can integrate AI APIs without complex backend development
Database structure can evolve as you learn more about user needs
Visual workflows match the validated user journeys you've already mapped
You can test AI features with real users quickly
But the crucial difference: every feature you build addresses a specific problem you've already validated through manual operations.
Manual First
Validate demand with human processes before building any AI features. This reveals real workflows and pain points.
Platform Choice
Bubble works well for AI prototypes, but only after you've proven what needs to be automated.
Integration Focus
Add AI to solve specific friction points you've discovered, not to create features that sound impressive.
User Journey
Map the complete user experience manually before automating any step with AI or no-code tools.
Using this validation-first approach completely changes prototype outcomes. Instead of building sophisticated solutions to imaginary problems, you create targeted AI features that solve real friction points.
For the marketplace client, manual validation revealed something crucial: their target market already had working solutions. The problem wasn't complex enough to require a marketplace – existing tools handled most use cases fine.
This discovery saved them months of development time and tens of thousands in budget. More importantly, it redirected their energy toward finding real problems worth solving.
When I apply this framework to other AI prototype projects, the results consistently show that manual validation prevents expensive dead ends. Projects that survive this phase tend to have much higher success rates because they're built on proven demand rather than assumptions.
The timeline advantage is counterintuitive: spending more time on validation actually speeds up overall development because you avoid building the wrong things.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After applying this approach across multiple AI prototype projects, here are the key lessons that consistently emerge:
Distribution beats product quality – Having real users matters more than sophisticated AI features
Manual processes reveal real workflows – You can't design good automation without understanding actual user behavior
Bubble shines for iteration – No-code platforms excel when you need to test and modify quickly
AI should solve validated problems – Every AI feature needs a proven purpose before development
Validation prevents scope creep – Clear user needs keep feature development focused
Budget follows validation – Investors and stakeholders support projects with proven demand
Speed comes from focus – Building the right things fast beats building everything slowly
The biggest mistake I see in AI prototyping is treating validation as a checkbox rather than a foundation. Real validation changes what you build, not just whether you build it.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups building AI prototypes:
Start with manual customer development, not feature development
Use proven SaaS metrics to validate AI feature value
Focus AI on user onboarding and retention pain points
For your Ecommerce store
For ecommerce stores considering AI features:
Validate AI recommendations through manual curation first
Test personalization concepts with simple segmentation
Use AI to automate proven customer service workflows