Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last year, I was working with a SaaS startup that had raised their seed round and wanted to build an AI-powered recommendation engine for their platform. The founder came to me excited about all the no-code AI tools he'd heard about, convinced we could ship something in weeks rather than months.
"We want to test if our AI idea works," he told me. "I heard Bubble can integrate machine learning now - can we build an MVP to validate this quickly?"
Here's the thing - I've seen this story play out dozens of times. Teams get excited about adding AI to their product, they start building, and six months later they're still debugging edge cases while their competitors ship simpler solutions that actually work.
But this time was different. Instead of building everything from scratch, I took a completely different approach that got us to market in 3 weeks instead of 3 months. The key? Treating AI as a service, not a science project.
In this playbook, you'll learn:
Why most teams over-engineer their AI MVP (and how to avoid this trap)
The exact workflow I use to integrate machine learning into Bubble apps without code
Which AI services actually work well with no-code platforms vs. which ones are just marketing hype
How to validate your AI concept before building expensive custom models
The surprising results from choosing pre-built AI over custom development
If you're building an MVP and considering adding AI features, this approach could save you months of development time and thousands in costs.
Industry Reality
What every founder thinks about AI in MVPs
Walk into any startup accelerator today and you'll hear the same advice: "AI is the future, you need machine learning in your product to stay competitive." The conventional wisdom goes something like this:
Start with a custom AI model - Build something unique that competitors can't replicate
Hire AI talent early - Get data scientists and ML engineers on your team
Collect lots of data - The more data you have, the better your model will be
Focus on accuracy metrics - Optimize for the highest possible model performance
Build your own infrastructure - Control your ML pipeline from training to deployment
This advice exists because it's what works for companies like Google, Netflix, and Amazon. These companies have massive datasets, dedicated AI teams, and years to perfect their models. The success stories are compelling.
But here's where this conventional wisdom falls apart for MVPs: you're not Google. You're trying to validate a business idea as quickly and cheaply as possible. Building custom ML models is the opposite of fast and cheap.
Most founders don't realize that the biggest tech companies didn't start with AI. Netflix began with DVD-by-mail, Amazon started as a bookstore, and Google was just a better search engine. They added sophisticated AI after they had proven their core business model and had the resources to invest in it properly.
The real problem with the "AI-first" approach for MVPs? You end up solving the wrong problem. Instead of figuring out if customers actually want your product, you spend months optimizing algorithms for a product that might not have product-market fit.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
So there I was with this SaaS client who wanted to build a recommendation engine for their B2B platform. Think of it like a smart matching system that would suggest relevant tools and resources to users based on their behavior and profile data.
The founder had been reading about Bubble's AI capabilities and was convinced we could build something sophisticated quickly. "It's just APIs, right?" he said. "We connect some machine learning service and we're done."
My first instinct was to do what I always did - start researching custom ML solutions. I spent days looking into TensorFlow.js integration, exploring AWS SageMaker APIs, and even considering building a Python backend to handle the heavy lifting.
The more I dug in, the more complex it became. We'd need:
Data preprocessing workflows
Model training pipelines
API endpoints for predictions
Error handling for edge cases
Performance optimization
Three weeks into the research phase, we hadn't written a single line of code. The client was getting antsy, and I realized we were falling into the same trap I'd seen dozens of times before - over-engineering the solution before we even knew if anyone wanted it.
That's when I had my "aha" moment. Instead of building a recommendation engine, what if we just built the interface and faked the intelligence behind it? Not permanently, but just long enough to test if users actually engaged with recommendations at all.
But even that felt too manual. There had to be a middle ground between "build everything from scratch" and "fake it completely." That's when I started looking into pre-built AI services that could give us real machine learning capabilities without the custom development overhead.
The breakthrough came when I realized we weren't trying to build the next Netflix recommendation algorithm. We were trying to validate whether our users would click on suggested content at all. Completely different problem, much simpler solution.
Here's my playbook
What I ended up doing and the results.
Here's exactly what I did to integrate machine learning into this Bubble MVP, step by step:
Step 1: Chose Pre-Built AI Over Custom Models
Instead of building our own recommendation engine, I integrated three existing AI services through Bubble's API connector:
OpenAI's Embedding API - For understanding content similarity
Pinecone - For vector storage and similarity search
Bubble's built-in database - For user behavior tracking
Step 2: Built the Data Pipeline in Bubble
I created workflows that automatically:
Captured user interactions (page views, clicks, time spent)
Sent content descriptions to OpenAI to generate embeddings
Stored these embeddings in Pinecone for fast similarity search
Updated user profiles based on their behavior patterns
Step 3: Created the Recommendation Logic
The actual "AI" was surprisingly simple. When a user visited the platform:
Bubble grabbed their interaction history from the database
Sent a query to Pinecone to find similar content
Ranked results based on recency and user behavior patterns
Displayed the top 5 recommendations in the UI
Step 4: Built the Learning Loop
Every time a user interacted with a recommendation, Bubble:
Logged whether they clicked, ignored, or dismissed it
Updated their preference weights in the database
Fed this data back into future recommendation queries
The Key Insight: AI as Assembly, Not Architecture
The breakthrough was treating machine learning like Lego blocks instead of rocket science. Instead of building a custom neural network, I assembled existing AI services that were already trained and optimized by teams with much more data and expertise than we had.
OpenAI's embeddings understood content similarity better than anything we could train in months. Pinecone handled vector search faster than any database we could build. Bubble managed the user interface and business logic perfectly.
The entire "AI" system was really just smart API orchestration - but it worked. Users got relevant recommendations, the system learned from their behavior, and we shipped in 3 weeks instead of 3 months.
Smart Assembly
Treating AI services like Lego blocks instead of building from scratch - saved months of development
API Orchestration
Using Bubble's workflow system to connect multiple AI services seamlessly
Validation First
Testing user engagement with recommendations before optimizing the algorithm
Learning Loop
Building feedback mechanisms that improve recommendations over time
The results were better than anyone expected:
Development Time: 3 weeks to working prototype vs. the estimated 3+ months for custom development
User Engagement: 34% of users clicked on at least one recommendation in their first session. The client's original hope was 15%.
Cost Efficiency: Total AI service costs were $47/month for the first 1,000 users. Building custom infrastructure would have cost thousands upfront plus ongoing maintenance.
Iteration Speed: We could test new recommendation strategies in hours, not weeks. Changed the similarity algorithm 8 times in the first month based on user feedback.
But the most surprising result was how "smart" the system felt to users, even though it was relatively simple under the hood. Users started commenting on how the platform "really understood" their needs - which taught us that perception of intelligence matters more than actual complexity.
The client raised their Series A six months later, and the recommendation system was specifically mentioned by two investors as a differentiating factor. Not because it was technically sophisticated, but because it demonstrated product-market fit and user engagement.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons from integrating ML into this Bubble MVP:
Start with pre-built AI services - They're trained on more data than you'll ever have and maintained by teams who specialize in ML
Focus on user experience, not algorithm perfection - A 70% accurate system that users love beats a 95% accurate system they never see
Build feedback loops from day one - Your AI gets smarter over time if you capture and use user behavior data
Treat AI as a feature, not the product - The recommendation system supported the core platform, it didn't replace it
Test with real users immediately - Academic metrics don't matter if people don't engage with your AI features
Keep it simple initially - You can always add complexity later, but you can't easily simplify an over-engineered system
Budget for API costs - Pre-built services cost money per use, but it's usually much cheaper than building and maintaining custom infrastructure
The biggest lesson? AI in MVPs should accelerate validation, not delay it. If your machine learning integration is taking months to build, you're probably solving the wrong problem.
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 ML capabilities:
Start with user behavior tracking and simple recommendation systems
Use embedding APIs for content similarity and search improvements
Focus on features that increase user engagement and retention
Build analytics dashboards to track AI feature performance
For your Ecommerce store
For ecommerce stores integrating machine learning:
Product recommendation engines using collaborative filtering APIs
Dynamic pricing optimization based on demand patterns
Inventory forecasting using historical sales data
Customer segmentation for personalized marketing campaigns