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 sophisticated machine learning platform using Bubble's no-code tools. The budget was substantial, the technical challenge seemed interesting, and it would have been one of my biggest projects to date.
I said no.
Not because Bubble is bad - it's actually incredibly powerful for rapid prototyping. But because they were asking the wrong question entirely. They wanted to know if Bubble could handle their ML requirements, when what they really needed to validate was whether anyone wanted their product in the first place.
Here's what most founders miss about building ML apps: the technology stack is rarely the bottleneck. The real challenge is proving demand before you build anything complex. Through working on multiple AI MVP projects, I've learned that the best "machine learning app" is often the one that starts without any machine learning at all.
In this playbook, you'll discover:
Why Bubble's strengths don't align with ML development workflows
My alternative approach that validates ML ideas in days, not months
The manual-first strategy that reveals real user needs
When to actually introduce ML into your validated concept
Real examples from AI startup projects I've consulted on
If you're considering building an AI MVP or evaluating no-code platforms for ML, this experience-based guide will save you months of wasted development time.
Industry Reality
What every no-code enthusiast believes about ML apps
The no-code movement has created this seductive narrative that you can build anything without developers. Bubble, in particular, markets itself as capable of handling complex applications. And technically, that's true.
Here's what the typical advice looks like:
"Bubble can integrate with any API" - Connect to OpenAI, TensorFlow serving, or custom ML endpoints
"No-code speeds up development" - Get your ML app to market faster than traditional coding
"Perfect for non-technical founders" - Build sophisticated AI tools without hiring developers
"Iterate quickly on features" - Rapid prototyping means faster product-market fit
"Cost-effective solution" - Avoid expensive development teams for early-stage products
This conventional wisdom exists because no-code tools genuinely have lowered barriers to building functional applications. Success stories of Bubble apps processing millions in revenue make it seem like a silver bullet for any startup idea.
But here's where this logic breaks down for ML applications: you're optimizing for the wrong thing. Most founders think their biggest risk is technical execution, when it's actually market validation. The question isn't "Can I build this?" - it's "Should I build this?"
Machine learning adds another layer of complexity that no-code platforms struggle with: data quality, model training, performance optimization, and regulatory compliance. These aren't UI problems you can drag-and-drop your way out of.
The result? Founders spend months building sophisticated Bubble apps with ML integrations, only to discover their core assumptions about user behavior were completely wrong.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When this client contacted me about their marketplace platform with ML-powered matching algorithms, they had everything figured out except the most important part: whether anyone actually wanted it.
Their vision was compelling - a two-sided platform that would use machine learning to intelligently match buyers with sellers in their niche market. They'd researched Bubble's capabilities extensively, found plugins for API integrations, and even had mockups showing how the ML recommendations would display.
The red flag hit me immediately: "We want to test if our idea works."
They had no existing audience, no validated customer base, no proof of demand. Just an idea and enthusiasm about the technology. This is where most ML projects go wrong - they start with the solution instead of the problem.
I've seen this pattern repeatedly in AI product development. Founders get excited about ML capabilities and assume that's what users want, without ever testing the underlying value proposition manually.
During our initial call, I asked them a simple question: "Have you tried matching buyers and sellers manually?" The answer was no. They wanted to build automation for a process they'd never proven worked in the first place.
This is the fundamental disconnect with ML applications. The technology feels so sophisticated that founders assume it must be valuable. But sophisticated technology solving the wrong problem is just expensive failure.
That's when I realized they weren't looking for a Bubble consultant - they needed a completely different approach to validation.
Here's my playbook
What I ended up doing and the results.
Instead of building their ML platform, I recommended something that initially shocked them: don't build anything at all for the first month.
Here's the exact process I suggested, which I now use for any client considering ML applications:
Week 1: Manual Matching Experiment
Create a simple landing page explaining the value proposition. When people sign up, manually review their needs and match them with potential partners. Use email, phone calls, whatever it takes. Track how many matches actually convert to transactions.
Week 2-3: Process Documentation
Document every step of successful manual matches. What information do you need? What patterns emerge? Which matching criteria actually matter versus what you assumed would matter? This becomes your ML training data specification.
Week 4: Demand Validation
Scale the manual process. If you can't handle the volume manually, you've proven demand exists. If there's no volume, you've saved months of development time on the wrong idea.
Only after proving the manual process works do you automate it. And here's the key insight: your first automation shouldn't be ML at all.
Start with simple rule-based matching. "If buyer wants X and seller offers X in the same geographic region, create a match." This handles 80% of cases and can be built in any platform, including Bubble if you want.
Machine learning becomes valuable only when:
You have enough data to train meaningful models (thousands of successful matches)
Simple rules can't handle the complexity you've validated users actually need
The ML improvement translates to measurable business value
For this specific client, we implemented the manual process using Zapier workflows and Google Sheets. Total setup time: 2 days. Total cost: under $50/month.
The result revealed something fascinating: users didn't want "smart" matching at all. They wanted transparency into why matches were suggested and control over the criteria. The ML sophistication they'd planned would have actually reduced user satisfaction.
Validation First
Don't build ML until you've proven the manual process works at scale
Process Mapping
Document every step of successful manual operations - this becomes your automation specification
Simple Rules
Start with basic rule-based automation before adding ML complexity
Data Quality
ML is only as good as the training data from your manual validation phase
The manual validation approach revealed insights that would have been impossible to discover through building first:
User Behavior: People wanted to see potential matches immediately, not wait for "intelligent" processing
Success Metrics: Match accuracy mattered less than match speed and transparency
Scalability: 90% of successful matches followed 3 simple rules that didn't require ML
Business Model: Revenue came from premium listing features, not matching sophistication
Most importantly, we validated the core business model in 30 days instead of spending 6 months building a platform that might have solved the wrong problem.
This approach has since become my standard recommendation for any ML product. The manual phase isn't a shortcut - it's the most important part of product development.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After applying this "manual-first" approach across multiple AI projects, here are the key lessons that consistently emerge:
Technology assumptions are usually wrong - What founders think needs ML rarely does
User behavior trumps algorithms - People care more about control and transparency than "intelligence"
Simple solutions scale better - Rule-based systems are easier to debug, explain, and improve
Manual processes reveal edge cases - You discover real-world complexity that no amount of planning anticipates
Data quality matters more than quantity - Manual validation creates cleaner training data for eventual ML
Business model clarity comes first - Understanding how you make money is more important than how smart your algorithms are
Platform choice becomes obvious - Once you know what you're building, the technical decisions are straightforward
The biggest mistake I see with ML projects is treating technology as the solution instead of a tool. Bubble isn't bad for ML apps - but most ML apps don't need to exist at all.
When you do need ML, start with the simplest possible implementation. Bubble can absolutely handle API calls to ML services, but by then you'll know exactly what you need instead of guessing.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups considering ML features:
Validate manually before automating anything
Start with rule-based systems, not ML
Focus on user value, not algorithm sophistication
Use AI PMF frameworks for validation
For your Ecommerce store
For ecommerce stores exploring ML recommendations:
Test manual product recommendations first
Simple "customers also bought" rules often outperform ML
Focus on data collection before algorithm optimization
Consider AI automation for operations, not customer-facing features