Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last year, a potential client approached me with what seemed like every no-code developer's dream: build a sophisticated two-sided AI marketplace platform using Bubble. The budget was substantial, the technical challenge was exciting, and they were eager to deploy cutting-edge AI features.
I said no.
Not because I couldn't deliver—Bubble's AI integrations are powerful, and deployment has never been easier. But because their opening statement revealed everything wrong with how most founders approach AI MVP development in 2025: "We want to see if our AI idea works."
They had no existing audience, no validated customer base, no proof anyone wanted their specific AI solution. Just enthusiasm for deploying AI technology and faith that Bubble could make it happen fast.
This conversation changed how I think about AI MVP deployment entirely. While everyone's rushing to build and deploy AI-powered platforms, most are solving the wrong problem first. Here's what you'll learn:
Why deploying on Bubble might be the wrong first step (even though it's powerful)
My one-day deployment framework that proves AI demand without code
When to actually use Bubble for AI MVP deployment (and when to wait)
The deployment strategy that saved me from a costly mistake
How to scale from manual validation to full Bubble deployment
This approach has helped me guide multiple clients away from expensive development cycles toward proven demand before deployment.
Common wisdom
What Every AI Founder Gets Wrong About MVPs
The startup world loves to talk about AI MVP development, and the advice is always the same. Pick a no-code platform like Bubble, integrate some AI APIs, deploy fast, iterate quickly. The mantra is "build, measure, learn"—get your AI product in front of users as quickly as possible.
Here's what everyone recommends for AI MVP deployment:
Choose your no-code platform (Bubble, Webflow, Framer) and get familiar with AI integrations
Design your AI workflow using visual builders and API connectors
Integrate AI services like OpenAI, Claude, or custom models through APIs
Deploy and test with real users to gather feedback and iterate
Scale based on data from actual user interactions with your AI features
This advice isn't wrong—it's actually quite practical. AI tools and no-code platforms have made sophisticated deployment accessible to non-technical founders. You can genuinely build impressive AI functionality without coding.
But here's the problem with this approach: it assumes your biggest constraint is building the AI functionality, when your actual constraint is usually proving anyone wants what you're building. The advice optimizes for deployment speed when it should optimize for learning speed.
In 2025, with AI capabilities becoming commoditized and no-code tools extremely powerful, the constraint isn't "Can we build it?" It's "Should we build it?" Most founders skip this question entirely and jump straight to deployment.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The client who approached me had exactly this mindset. They'd done their research, knew Bubble could handle their technical requirements, and were ready to invest months in building their two-sided AI marketplace.
Their concept was actually solid: connect AI service providers with businesses needing AI solutions. Think of it as a marketplace where companies could find verified AI consultants, pre-built AI workflows, and custom AI development services.
But when I dug deeper into their preparation, I discovered some critical gaps:
No target customer interviews - They assumed demand existed based on AI hype
No existing audience - They planned to "build it and market it"
No manual validation - They'd never tried connecting AI providers with businesses manually
No pricing validation - They weren't sure what either side would pay
The red flag moment came when they said: "We want to build the platform to test if our idea works." That's backwards thinking. If you're truly testing an idea, you shouldn't need a platform.
I've seen this pattern repeatedly. Founders get excited about deployment capabilities—whether it's Bubble, AI APIs, or other no-code tools—and mistake building speed for validation speed. They end up with beautiful, functional products that nobody uses.
This is when I realized that most AI MVP advice gets the sequence wrong. Distribution and validation should come before deployment, not after.
Here's my playbook
What I ended up doing and the results.
Instead of accepting their project, I proposed something that initially shocked them: build your AI MVP without any platform in one day.
Here's the framework I shared with them, which I now use for any AI MVP deployment decision:
Day 1: Manual AI Marketplace Test
Create a simple landing page explaining your AI marketplace concept
Start manual outreach to potential AI service providers via LinkedIn/email
Simultaneously reach out to businesses that might need AI services
Manually broker the first connection via email or phone calls
Week 1-2: Prove the Matching Process
Handle 5-10 manual matches between AI providers and businesses
Document the actual workflow of connecting these parties
Validate pricing by facilitating real transactions manually
Identify pain points in the manual process that automation could solve
Month 1: Build Your Audience Before Your Platform
Create a waitlist of validated AI providers and businesses
Document case studies from your manual matches
Refine your value proposition based on actual user feedback
Only then consider Bubble deployment to automate proven processes
The key insight: your first MVP should be your marketing and sales process, not your product. Technology comes after you've proven demand exists and you understand the exact workflow to automate.
When you do eventually deploy on Bubble, you'll have:
Validated demand from real customers willing to pay
Clear user stories based on actual workflow experience
Pricing validation from real transactions
A built-in audience ready to use your automated platform
Pre-validation
Skip expensive development cycles by proving demand through manual processes before building anything.
Distribution first
Build your audience and prove your matching process manually before creating the automated platform.
Workflow mapping
Document exactly how the manual process works so you know precisely what to automate in Bubble.
Ready-made users
Launch your Bubble deployment to an audience that's already validated and waiting for the automated solution.
The client initially pushed back on this approach. "But we could have the platform built in weeks with Bubble," they argued. "Why spend time on manual processes?"
Here's what I explained: those weeks of Bubble development assume you know exactly what to build. But without validation, you're likely to build the wrong thing efficiently.
The manual approach I recommended would have given them several key insights in just 30 days:
Real demand validation - Are businesses actually willing to pay for AI marketplace services?
Pricing discovery - What commission rates work for both sides?
Feature priorities - What's essential vs. nice-to-have in the matching process?
User experience insights - How do real users want to interact with this service?
Unfortunately, they decided to work with a different developer who promised to build their full platform. Six months later, I learned through mutual connections that they'd spent their entire budget building a sophisticated Bubble application that struggled to gain traction.
The platform worked beautifully from a technical standpoint, but they couldn't find enough users on either side to create the network effects they needed. They're now pivoting to... you guessed it... manual outreach to build their initial user base.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
This experience taught me five crucial lessons about AI MVP deployment that have guided every project since:
Technology capability isn't the bottleneck anymore - Tools like Bubble can build almost anything, so the constraint shifts to knowing what to build
Manual processes reveal the real workflow - You can't design good automation until you understand the actual process users need
Distribution beats deployment - A simple solution with users trumps a sophisticated platform without them
AI MVPs need human validation first - Even AI solutions require understanding real human problems and workflows
Build your audience, then build your automation - Deployment should automate proven demand, not create it
The biggest misconception in AI MVP development is that the goal is to test your technology. Actually, the goal is to test your business model. Technology comes after you've proven people want what you're offering.
When you do deploy on platforms like Bubble, you'll be automating processes you already know work, rather than hoping your automated process will work. That's the difference between successful AI MVP deployment and expensive experimentation.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS founders considering AI MVP deployment:
Start with manual validation before any Bubble development
Use simple landing pages to test demand first
Build your initial user base through direct outreach
Document workflows manually before automating them
Deploy on Bubble only after proving manual demand
For your Ecommerce store
For ecommerce entrepreneurs exploring AI features:
Test AI recommendations manually with existing customers first
Validate pricing for AI-powered services before development
Use email/chat to simulate AI features before building them
Deploy AI tools only after manual process proves value
Focus on customer workflow improvement over impressive technology