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 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.
But here's the twist - I recommended they use Bubble.io for their AI prototype instead. Not because I was being lazy, but because I'd learned something crucial about MVP development in the AI era: the constraint isn't building anymore, it's knowing what to build and for whom.
While everyone debates whether to use React or Vue, whether to build custom APIs or use existing services, they're missing the fundamental point. Your first MVP shouldn't take three months to build - it should take three days.
In this playbook, you'll discover:
Why no-code platforms like Bubble are perfect for AI prototyping
The real reason most AI MVPs fail (hint: it's not technical)
My exact framework for validating AI features before writing code
How to integrate AI services into Bubble without any coding
When to graduate from Bubble to custom development
Ready to build your AI prototype in days, not months? Let's dive into why Bubble might be your secret weapon.
Industry Reality
What the AI startup world keeps getting wrong
Walk into any startup accelerator or tech meetup, and you'll hear the same advice about AI prototyping: "Build it properly from day one." The conventional wisdom goes something like this:
Use Python and FastAPI for maximum AI library compatibility
Set up proper MLOps pipelines from the beginning
Design scalable architecture that can handle enterprise loads
Implement robust data pipelines and model versioning
Build custom APIs for complete control over AI functionality
This advice comes from a good place. Technical founders and developers want to avoid "technical debt" and ensure their AI applications can scale. VCs love hearing about "proprietary technology stacks" and "advanced machine learning infrastructure."
The problem? Most AI startups die before they ever need to scale.
According to CB Insights, 70% of AI startups fail within the first two years. But here's the kicker - it's rarely because of technical limitations. It's because they spent six months building the "perfect" AI application that nobody wanted.
The real issue isn't whether your AI model can handle 10,000 requests per second. It's whether even 10 people want to use it in the first place. Yet founders keep optimizing for scale before they've achieved fit.
Meanwhile, companies like OpenAI proved you can start with simple interfaces and scale later. ChatGPT's initial interface was basically a text box - built in days, not months.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Here's the story I don't usually tell. Before I developed my current approach to AI prototyping, I made the same mistake everyone else makes.
A fintech startup hired me to build their "AI-powered financial advisor." The concept was solid: use machine learning to analyze spending patterns and provide personalized investment recommendations. They had a $50K budget and a 3-month timeline.
Like any "proper" developer, I started with the technical foundation. Python backend, PostgreSQL database, custom ML pipeline, React frontend. I spent weeks setting up the perfect development environment. Docker containers, CI/CD pipelines, the works.
Three months later, we had a beautiful, scalable AI application. The code was clean, the architecture was sound, and the ML models were impressively accurate. But we had exactly zero users who actually wanted to use it.
The client had burned through their budget, and we'd built an AI solution for a problem that didn't really exist in the way we'd imagined. Users wanted simple budgeting tools, not complex AI recommendations. They wanted transparency, not black-box algorithms making financial decisions.
That failure taught me something crucial: in AI development, speed of iteration beats technical perfection every single time.
The next AI project I worked on, I took a completely different approach. Instead of starting with code, I started with Bubble.io and external AI APIs. In one week, we had a working prototype. In two weeks, we had real users testing real functionality. By month three, we knew exactly what to build - and more importantly, what not to build.
That's when I realized: Bubble isn't a compromise for AI prototyping. It's actually the superior choice for most AI MVPs.
Here's my playbook
What I ended up doing and the results.
After that expensive lesson, I developed a systematic approach to AI prototyping that I now use for every AI project. Here's the exact framework that's saved clients thousands of dollars and months of development time.
Day 1: Core UI and User Flow
Instead of worrying about AI models, I start with the user experience. In Bubble, I can build the entire user interface in hours, not days. I create the main screens, set up user authentication, and design the core workflow. The key insight? Most AI applications are just smart forms with intelligent outputs.
For a recommendation engine, this means: input form, loading state, results display. For a chatbot: chat interface, message history, user settings. The AI is just one component in a larger user experience.
Day 2: External AI Integration
This is where Bubble's API connector becomes your best friend. Instead of training custom models, I integrate with existing AI services:
OpenAI API for natural language processing
Google Cloud Vision for image analysis
Hugging Face for specialized models
AssemblyAI for speech recognition
The magic happens in Bubble's workflow editor. I can chain multiple AI services together, process the results, and present them to users - all without writing a single line of code. Need to analyze sentiment, then generate a response, then translate it? That's three API calls in one workflow.
Day 3: Data Flow and Testing
Bubble's database is perfect for MVP data needs. I set up data types for users, AI requests, and results. Then I add analytics tracking to understand how users interact with the AI features. Most importantly, I implement feedback loops - ways for users to rate AI outputs and improve the system over time.
By day three, I have a fully functional AI application that real users can test. Not a demo, not a prototype - a working product.
The Iteration Advantage
Here's where Bubble's real power shows. When users inevitably request changes (and they will), I can implement them in minutes. Want to adjust the AI prompt? Change it in the workflow. Need to add a new input field? Drag and drop. Want to try a different AI service? Swap the API endpoint.
In traditional development, each of these changes requires code modifications, testing, and deployment. In Bubble, they're configuration changes that happen instantly.
I've used this framework for AI applications ranging from content generators to predictive analytics tools. The pattern is always the same: build fast, test real user behavior, iterate based on feedback. By the time most custom-coded solutions finish their technical setup, I've already validated the core AI functionality and user demand.
Speed Advantage
Build functional AI prototypes in days, not months
Cost Efficiency
Use external AI APIs instead of training custom models
User Validation
Test real user behavior before committing to technical architecture
Iteration Speed
Make changes instantly without code deployment cycles
The results speak for themselves. Using this Bubble-first approach, I've helped clients validate AI concepts in weeks instead of months, spending thousands instead of tens of thousands.
One recent client was considering building a custom AI content moderation tool. Using Bubble and OpenAI's moderation API, we had a working prototype in 3 days. After two weeks of user testing, we discovered that users actually needed content enhancement, not moderation. Pivoting in Bubble took one afternoon. A custom solution would have required starting over completely.
Another client wanted to build an AI-powered customer support chatbot. Traditional estimates were 2-3 months and $75K. Using Bubble with OpenAI's API, we had the first version running in one week for under $2K. More importantly, we learned that customers preferred AI-assisted human agents over fully automated responses - an insight that completely changed their product strategy.
The pattern is consistent: Bubble eliminates the technical risk and lets you focus on the product risk. Instead of wondering "can we build this?" you can focus on "should we build this?"
Of the dozen AI prototypes I've built with this approach, 8 pivoted significantly based on user feedback. Only 3 proceeded to custom development. That means 75% of projects avoided expensive technical rebuilds by validating with Bubble first.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After dozens of AI projects using this approach, here are the most important lessons I've learned:
Technical complexity kills more AI startups than market competition. Every hour spent on infrastructure is an hour not spent understanding users.
External AI APIs are usually better than custom models. OpenAI, Google, and others have spent billions on AI research. Your startup probably can't do better in version 1.
Users care about outcomes, not algorithms. They don't care if you're using GPT-4 or a simple rules engine - they care if it solves their problem.
The fastest way to improve AI is through user feedback. Real usage data beats theoretical optimization every time.
Most AI features are actually UI/UX problems. The challenge isn't making AI work - it's making AI results useful and understandable.
Bubble scales further than most founders think. Companies have built million-dollar businesses entirely on Bubble. Your AI MVP probably doesn't need custom code.
Speed of iteration is the ultimate competitive advantage. While competitors debate technical architecture, you're learning from real users and improving your product.
The biggest mistake I see founders make is treating AI prototyping like traditional software development. AI products need to be discovered, not just built. Bubble gives you the speed to discover what actually works before committing to expensive custom development.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups looking to add AI features:
Start with external AI APIs before building custom models
Use Bubble to test AI workflows with real users quickly
Focus on user value, not technical complexity
Implement feedback loops to improve AI outputs
For your Ecommerce store
For ecommerce stores considering AI features:
Test AI product recommendations before custom development
Use chatbots for customer support validation
Try AI-powered search and personalization via APIs
Validate AI features that actually drive sales