Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last month, a potential client approached me with an exciting opportunity: build a two-sided marketplace platform with 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.
Here's why — and what this taught me about the real purpose of MVPs in 2025. The client came to me excited about the no-code revolution and new AI tools like Bubble. They'd heard these tools could build anything quickly and cheaply. They weren't wrong — technically, you can build a complex AI-powered platform with these tools.
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 for AI.
In this playbook, you'll learn:
Why most founders approach AI development backwards
The real constraints of building AI apps with Bubble
My framework for determining when to use no-code vs custom development
Specific AI integrations that work (and don't work) in Bubble
How to validate AI features before building anything
This isn't about the technical "how" — it's about the strategic "why" that most founders miss when they get excited about AI possibilities.
Reality Check
What every founder believes about no-code AI
The no-code AI narrative is everywhere right now. You've probably seen the headlines: "Build an AI startup in 24 hours with Bubble!" or "No-code AI: The future of rapid prototyping." The promise is seductive — combine the speed of no-code platforms with the power of AI, and you can supposedly build the next unicorn from your laptop.
Here's what the conventional wisdom tells you:
Bubble can integrate with any API — Therefore, you can connect it to OpenAI, Claude, or any AI service
No-code speeds up development — You'll ship faster than traditional coding
AI makes everything possible — Complex features become simple with the right prompts
MVP-first approach — Build quickly, test, iterate
Cost-effective validation — Test ideas without hiring developers
This advice exists because it's partially true. Bubble can integrate with AI APIs. You can build functional prototypes quickly. The tools have democratized access to powerful technologies.
But here's where this conventional wisdom falls short: it confuses what's technically possible with what's strategically smart. Just because you can build something doesn't mean you should. The real question isn't "Can I use Bubble to create an AI app?" — it's "Should I?"
Most founders get caught up in the technical excitement and miss the bigger picture. They're solving the wrong problem first.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When that client approached me about their AI-powered marketplace, I recognized a pattern I'd seen repeatedly in my consulting work. They wanted to test if their idea was worth pursuing, but they were planning to spend months building a complex platform to find out.
The situation was classic: entrepreneurs confusing validation with development. They had identified a real problem in their industry — inefficient matching between buyers and sellers — and believed AI could solve it better than existing solutions. Their enthusiasm was genuine, and their market research looked solid on paper.
But here's what they were missing: they had no existing relationship with their target customers. No email list, no social media following, no industry connections. They were essentially asking me to build a sophisticated tool for people they'd never spoken to.
This is where my experience with SaaS development kicked in. I've seen too many founders build beautiful, functional products that nobody uses. The issue wasn't their technical execution — it was their validation process.
I explained my philosophy: "If you're truly testing market demand, your MVP should take one day to build — not three months." Yes, even with AI and no-code tools, building a functional two-sided platform takes significant time. But your first MVP shouldn't be a product at all.
Their reaction was telling. They pushed back, explaining how Bubble could handle the complexity, how AI would differentiate them, how quickly they could iterate. They were focused on the solution, not the problem validation.
That's when I knew they weren't ready for what I typically offer through my growth consulting approach.
Here's my playbook
What I ended up doing and the results.
Instead of taking their project, I shared my alternative approach — what I call the "Manual MVP Framework" for AI-powered ideas. This framework has saved my clients thousands of dollars and months of development time.
Phase 1: Demand Validation (Week 1)
I recommended they start with a simple landing page explaining their value proposition. Not a Bubble app, not AI integration — just a clear description of what problem they'd solve and for whom. The page should capture emails from interested users.
But here's the key: the "AI features" they wanted to build could be simulated manually. Instead of training algorithms to match buyers and sellers, they could do it themselves initially. This manual process would teach them what good matches actually look like before automating anything.
Phase 2: Manual Operations (Weeks 2-4)
My playbook called for manual matchmaking via email and phone calls. Yes, it doesn't scale — that's the point. The goal is learning what customers actually want, not building scalable systems.
During this phase, they'd document every decision: What criteria matter for matches? How do customers prefer to communicate? What information is actually needed? These insights become the foundation for any future AI development.
Phase 3: Simple Automation (Month 2)
Only after proving demand manually would I recommend building anything. And even then, the first automation wouldn't be AI — it would be simple workflow tools like Zapier or basic Bubble forms.
The AI piece comes last, not first. By this point, they'd understand their users well enough to know which AI features would actually provide value versus which ones just sound impressive.
The Bubble Integration Strategy
When the time comes to actually build, Bubble works well for the wrapper — user interfaces, data management, payment processing. But the AI logic often works better as external API calls to specialized services.
My approach treats Bubble as the presentation layer and keeps complex AI processing separate. This maintains flexibility and makes debugging much easier.
Testing Framework
Document every manual decision to inform future AI automation
Validation Tools
Use landing pages and manual processes before building complex features
Integration Strategy
Keep AI processing separate from Bubble's presentation layer
Scaling Mindset
Start manual, automate only proven valuable processes
The results of this approach speak for themselves, though not in the way most founders expect. When I share this framework, about 70% of potential AI projects get abandoned — and that's a good thing.
Those founders discover through manual testing that their assumptions were wrong. Maybe the matching criteria they thought were important don't actually matter to users. Maybe the AI features they were excited about solve problems customers don't have.
The 30% who make it through manual validation build much stronger products. They understand their users deeply, their AI features solve real problems, and they avoid the technical complexity trap that kills so many no-code projects.
One client who followed this approach discovered that their "AI-powered recommendation engine" could be replaced by three simple dropdown menus. They saved months of development and built something users actually preferred.
Another learned that their marketplace didn't need AI matching at all — what users really wanted was better search filters and seller verification. They built this in Bubble in two weeks instead of spending months on machine learning.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key insights I've gained from guiding founders through AI validation:
AI is often a solution looking for a problem — Most founders start with "let's add AI" instead of "what problem needs solving?"
Manual processes teach you what to automate — You can't build good AI without understanding the human decision-making process first
Bubble excels at interfaces, not intelligence — Use it for user experience, not complex logic
Validation comes before optimization — Prove people want the outcome before perfecting the process
Complexity kills iteration speed — The more moving parts, the harder it becomes to test and improve
Users care about results, not technology — They want their problem solved, whether that's through AI or spreadsheets
Technical constraints force creative solutions — Bubble's limitations often lead to better user experiences
The biggest mistake I see founders make is treating no-code AI development like traditional software development. They focus on features and technical capabilities instead of user outcomes and problem validation.
If you're considering building an AI app with Bubble, start with the validation framework first. Only move to development after you've proven demand manually.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS Startups:
Start with manual user onboarding to understand what "smart" features actually matter
Use Bubble for dashboards and user management, external APIs for AI processing
Focus on one AI feature that directly improves user outcomes, not multiple "cool" features
Document all manual decisions to inform future automation priorities
For your Ecommerce store
For E-commerce Stores:
Test product recommendation logic manually before building AI systems
Use Bubble for customer-facing interfaces, keep inventory intelligence separate
Start with rule-based personalization before moving to machine learning
Focus on AI features that directly increase conversion or basket size