Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last year, I was working with a B2C e-commerce client who had over 3,000 products and was drowning in their own catalog. Customers were clicking around endlessly, leaving with empty carts because they couldn't find what they actually wanted. Sound familiar?
The client's first instinct was what most businesses do - hire a development team to build a custom AI recommendation engine. Six-figure budget, six-month timeline, and zero guarantee it would actually work. I had a different idea.
What if we could build a working AI recommendation system in days, not months, using no-code tools like Bubble?
Turns out, you can. And it might actually work better than the custom solution because you can iterate faster and test real user behavior immediately. This isn't about building a perfect AI system - it's about building something that works and improves your business metrics.
Here's what you'll learn from my experience:
Why no-code AI recommendation engines often outperform custom solutions
The exact Bubble workflow I built to analyze user behavior and generate recommendations
How to integrate third-party AI services without touching code
What metrics actually matter for recommendation system success
Common pitfalls that kill no-code AI projects before they start
The best part? This approach works for SaaS onboarding, content platforms, and any business that needs to help users discover what they actually want. Let me show you exactly how I built it and what I learned along the way.
Industry Reality
What Every Startup Founder Gets Told About AI
If you've ever mentioned wanting an AI recommendation system to a developer or agency, you've probably heard the same speech I've heard dozens of times. It goes something like this:
"You need proper machine learning infrastructure. Real AI requires data science expertise. No-code tools are just toys that can't handle serious AI workloads."
The industry loves to overcomplicate AI. Here's what the conventional wisdom tells you:
Hire a data science team - You need PhDs to build "real" AI
Build custom ML pipelines - Everything needs to be coded from scratch
Collect massive datasets - You need millions of data points before starting
Use enterprise-grade infrastructure - AWS SageMaker or bust
Plan for months of development - Good AI takes time to build properly
This advice exists because the AI industry has been dominated by tech giants and research institutions for years. The assumption is that if Netflix and Amazon need complex recommendation systems, so does everyone else.
But here's where this falls apart in practice: Most businesses don't need Netflix-level complexity. They need something that works today, improves user experience, and drives conversions. While you're spending six months building the perfect AI system, your competitors are testing and iterating with no-code solutions that already work.
The reality is that modern no-code platforms like Bubble have evolved way beyond simple CRUD apps. Combined with AI APIs from OpenAI, Anthropic, or specialized services, you can build sophisticated recommendation systems that deliver real business value in days, not months.
The question isn't whether no-code AI is "real" AI - it's whether it solves your business problem better than the alternative.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Here's the situation I walked into: This e-commerce client was running a Shopify store with over 3,000 products across dozens of categories. Their average session duration was terrible, bounce rate was through the roof, and conversion rates were stuck at around 1.2%.
The core problem wasn't the products - it was discovery. Customers would land on the homepage, see this massive catalog, feel overwhelmed, and leave. The ones who stayed would click through maybe 3-4 products before giving up.
The client's initial plan was exactly what you'd expect: hire a development team to build a custom recommendation engine. They'd gotten quotes ranging from $80k to $150k with timelines between 4-8 months. The proposed solution involved machine learning models, collaborative filtering, and all the buzzwords.
But I knew something they didn't - most of these custom recommendation systems fail not because the AI isn't sophisticated enough, but because they take so long to build that by the time they're live, the business has already moved on or the user behavior has changed.
I proposed a different approach: "What if we could have a working recommendation system live in two weeks instead of six months?"
The client was skeptical. They'd been conditioned to believe that good AI requires massive investment and development time. But they agreed to let me try a no-code approach first, mainly because the risk was so much lower.
This is where most no-code AI projects go wrong - people try to replicate complex systems instead of starting with simple, effective solutions. I didn't try to build Netflix's recommendation algorithm. I focused on solving one specific problem: helping users find products they'd actually want to buy.
Here's my playbook
What I ended up doing and the results.
Step 1: Understanding User Intent Through Bubble Workflows
Instead of starting with AI, I started with data collection. I built Bubble workflows to track specific user behaviors that indicated intent:
Time spent on product pages
Category browsing patterns
Cart additions (even if not purchased)
Search queries
Similar product views in the same session
The key insight: I wasn't trying to predict what users might want. I was identifying what they'd already shown interest in and finding similar products.
Step 2: Building the Recommendation Logic
Here's where most people overcomplicate things. Instead of building complex ML models, I used a combination of:
Rule-based recommendations - If someone views running shoes, show other running shoes
Behavior-based suggestions - Track what other users with similar browsing patterns bought
AI-enhanced descriptions - Used OpenAI API to analyze product descriptions and find semantic similarities
Step 3: The Bubble Implementation
I built the entire system using Bubble's workflow engine and database. The core architecture was surprisingly simple:
Data Table: User sessions with behavior tracking
Product Analysis Workflow: API calls to analyze product similarities
Recommendation Engine: Bubble workflows that ranked products based on multiple factors
Display Logic: Dynamic elements that showed recommendations throughout the site
Step 4: AI Integration Without Complexity
Rather than building ML models from scratch, I integrated existing AI services through APIs:
OpenAI for semantic product matching - Analyzed product descriptions to find similar items
Customer.io for behavior triggers - Automated follow-up based on browsing patterns
Zapier for data sync - Connected Bubble to Shopify and other tools seamlessly
The beauty of this approach was that each component could be tested and improved independently. If the semantic matching wasn't working well, I could swap it for a different service without rebuilding the entire system.
Step 5: Continuous Optimization Through A/B Testing
This is where no-code really shines. Within Bubble, I set up multiple recommendation algorithms running simultaneously:
Version A: Pure behavior-based recommendations
Version B: AI-enhanced semantic matching
Version C: Hybrid approach combining both
I could adjust the logic, test new approaches, and see results in real-time. No deployment cycles, no developer bottlenecks - just continuous improvement based on actual user data.
Technical Stack
Bubble workflows + OpenAI API + Zapier integrations for seamless data flow
Performance Metrics
Tracked click-through rates, conversion improvements, and user session duration
Testing Framework
A/B tested multiple recommendation algorithms simultaneously within Bubble
Scalability Planning
Built modular components that could handle growing product catalogs and user bases
The results weren't just good - they were immediate and measurable:
Within the first two weeks:
Average session duration increased from 2:30 to 4:15
Product page views per session went from 3.2 to 7.8
Cart abandonment rate dropped by 15%
After one month of optimization:
Conversion rate improved from 1.2% to 2.1%
Average order value increased by 23%
Users were discovering products they never would have found through traditional browsing
But here's what really surprised me: The no-code system was actually more effective than the custom solutions I'd seen at other clients. Why? Because we could iterate daily instead of waiting months for updates.
The client was amazed. They'd expected to wait half a year for a recommendation system, and instead had something working better than they'd imagined in two weeks. The total cost was about 95% less than the quoted custom development.
More importantly, when the system needed adjustments based on seasonal trends or new product categories, I could implement changes the same day instead of filing development tickets and waiting weeks for updates.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons I learned building AI recommendation systems with no-code tools:
Start with user behavior, not algorithms - Focus on what users are already telling you through their actions
Perfect is the enemy of good - A simple system that works today beats a perfect system that launches in six months
No-code doesn't mean no-strategy - You still need to understand the business problem and user psychology
API integration is your superpower - Leverage existing AI services instead of building everything from scratch
Test early and often - The ability to iterate quickly is more valuable than sophisticated algorithms
Data quality matters more than quantity - Clean, relevant data beats massive datasets
User experience trumps technical complexity - If it doesn't improve the customer journey, the AI is pointless
What I'd do differently: I would have started with even simpler logic and built complexity gradually. The temptation with no-code is to add features quickly, but sometimes less is more.
When this approach works best: E-commerce stores with 100+ products, SaaS platforms with multiple features, content sites with diverse offerings. Basically, anywhere users need help discovering what they want.
When to avoid it: If you have unlimited budget and time, or if your recommendation needs are extremely specialized (like financial trading algorithms). But honestly, that's maybe 5% of businesses.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups implementing this playbook:
Track feature usage patterns to recommend relevant tools
Use onboarding behavior to suggest next steps
Implement progressive feature discovery based on user maturity
A/B test recommendation placement within your product interface
For your Ecommerce store
For e-commerce stores applying this approach:
Start with "frequently bought together" recommendations
Implement category-based suggestions on product pages
Use cart abandonment data to improve recommendations
Test recommendation widgets on homepage and category pages