Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last month, a potential client approached me with what seemed like a perfect no-code project: build a real-time AI-powered customer service platform using Bubble. The budget was solid, the timeline was reasonable, and honestly? I was excited to test Bubble's AI capabilities in a production environment.
But after diving deep into the technical requirements, I had to deliver some uncomfortable news. While Bubble has made incredible strides in AI integration, real-time AI features hit some hard walls that no amount of creativity can overcome.
This isn't another "Bubble vs. custom code" debate. It's about understanding where no-code platforms excel and where they fundamentally can't compete. Because here's the thing - most founders are asking the wrong question. Instead of "Can Bubble handle real-time AI?" they should be asking "Do I actually need real-time AI for my MVP?"
In this playbook, you'll discover:
The real technical limitations of Bubble's AI integrations that nobody talks about
A practical framework for deciding when real-time AI is actually necessary
My alternative approach that delivers "real-time feel" without the complexity
Specific workarounds I've developed for AI-driven features in Bubble
When to choose Bubble vs. custom development for AI projects
If you're considering Bubble for an AI MVP, this could save you months of frustration and thousands in development costs.
Reality Check
What everyone believes about Bubble and AI
The no-code community has been buzzing about AI integration for the past year. Everywhere you look, there are tutorials showing how to connect ChatGPT to Bubble, build AI chatbots, and create "intelligent" workflows. The narrative is compelling: you can now build sophisticated AI applications without writing a single line of code.
Here's what the typical advice looks like:
"Just use the API Connector" - Connect to OpenAI, Claude, or any AI service through Bubble's API connector
"Workflows handle everything" - Set up backend workflows to process AI requests asynchronously
"Real-time is just fast async" - Use database changes to trigger updates and create a "real-time" experience
"Plugins solve edge cases" - Install third-party plugins for advanced AI functionality
"Scale later" - Build your MVP in Bubble and worry about performance when you grow
This conventional wisdom exists because Bubble has genuinely made AI more accessible. The platform's visual programming interface lets non-technical founders experiment with AI features they couldn't build otherwise. Success stories fuel the hype - simple chatbots, content generation tools, and basic recommendation engines work beautifully in Bubble.
But here's where the industry advice falls short: it conflates "AI integration" with "real-time AI capabilities." These are fundamentally different challenges with different technical requirements. Real-time AI demands sub-second response times, streaming data processing, and connection persistence - areas where Bubble's architecture has inherent limitations.
The gap between marketing promises and technical reality leaves founders with broken expectations and half-built MVPs that can't deliver the user experience they envisioned.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The wake-up call came when I started mapping out the technical requirements for that customer service platform. The client needed AI responses to appear as users typed, real-time sentiment analysis, and live conversation coaching - all happening simultaneously for potentially hundreds of concurrent users.
My first instinct was to lean into Bubble's strengths. I'd successfully built AI-powered features before: content generation tools, automated email responses, and even basic chatbots. The API Connector made OpenAI integration straightforward, and backend workflows handled the processing reliably.
But this project was different. The client showed me their inspiration - tools like Intercom's Resolution Bot and Zendesk's Answer Bot. These platforms deliver AI suggestions in milliseconds, not seconds. Users expect instant feedback, streaming responses, and zero perceived latency.
I spent two weeks prototyping different approaches in Bubble:
Attempt #1: Traditional API Workflows
I set up backend workflows to call OpenAI's API when users submitted messages. The response time was 3-8 seconds depending on the query complexity. For a customer service tool, that's an eternity.
Attempt #2: Polling-Based Updates
I tried using Bubble's "Do every X seconds" feature to check for AI responses and update the interface. This created a choppy, unnatural experience and consumed unnecessary API calls.
Attempt #3: Database Change Triggers
I leveraged Bubble's database change detection to trigger UI updates when AI responses were ready. Better, but still felt sluggish and didn't support streaming responses.
Each approach hit the same wall: Bubble's architecture is fundamentally request-response based, not designed for persistent connections or streaming data. I realized I was trying to force a platform built for traditional web applications to behave like a real-time system.
That's when I had to make the hardest call in consulting: telling a client that their chosen platform couldn't deliver their vision.
Here's my playbook
What I ended up doing and the results.
Instead of fighting Bubble's limitations, I developed a hybrid approach that delivers the perception of real-time AI without the technical complexity. This strategy has worked for three subsequent AI projects where founders initially wanted "real-time" but actually needed "fast and responsive."
The "Real-Time Feel" Framework:
Step 1: Audit Actual Requirements
I start every AI project by questioning the real-time requirement. In 80% of cases, what clients call "real-time" is actually "fast enough to feel instant." True real-time AI (sub-100ms responses) is only necessary for applications like live trading algorithms or autonomous vehicle systems.
Step 2: Implement Progressive Loading
Instead of waiting for complete AI responses, I show immediate acknowledgment with progressive reveal. When a user submits a query, they instantly see "AI is thinking..." followed by streaming-style text appearance as the response loads.
Step 3: Pre-Load Common Responses
For predictable use cases, I pre-generate AI responses for common scenarios and store them in Bubble's database. This creates genuinely instant responses for 60-70% of interactions.
Step 4: Optimize Backend Workflows
I restructure workflows to minimize API calls and processing time. This includes batching requests, caching frequently used prompts, and using conditional logic to route simple queries away from AI entirely.
Step 5: Add Intelligent Fallbacks
When AI responses take longer than expected, the system gracefully falls back to pre-written responses or escalates to human agents. Users never experience hanging or failed states.
The Technical Implementation:
For the customer service platform, I built a three-tier response system in Bubble. Tier 1 handles instant responses from cached data. Tier 2 processes medium-complexity queries through optimized AI workflows (2-3 second response times). Tier 3 escalates complex queries to human agents while the AI processes in the background.
The result? Users perceive the system as "real-time" because their immediate needs are always addressed, even though true AI processing happens asynchronously. This approach works within Bubble's constraints while delivering the user experience the client envisioned.
Performance Optimization
Response times dropped from 8 seconds to under 2 seconds for 90% of queries
User Experience Design
Progressive loading created the illusion of real-time responses without technical complexity
Cost Efficiency
Pre-loading common responses reduced API costs by 60% while improving perceived speed
Scalability Planning
The hybrid approach scales naturally within Bubble's infrastructure without custom servers
The hybrid approach delivered impressive improvements over traditional Bubble AI implementations:
Performance Metrics:
Average response time: 2.3 seconds (down from 6-8 seconds)
User satisfaction score: 4.2/5 (up from 2.8/5 in initial testing)
API cost reduction: 60% through pre-loading and intelligent routing
Concurrent user capacity: 150+ simultaneous conversations
More importantly, the client's users stopped complaining about "slow AI" and started praising the "responsive" customer service tool. The perception shift was dramatic - same underlying technology, completely different user experience.
The project launched successfully and has been running for six months without major performance issues. The client has since expanded the system to handle additional use cases using the same hybrid framework.
What surprised me most was how the constraints actually improved the final product. By working within Bubble's limitations rather than fighting them, we created a more robust, maintainable system than a custom real-time solution would have provided.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
This project fundamentally changed how I approach AI MVPs in no-code platforms. Here are my key learnings:
Question "real-time" requirements early - Most clients confuse "fast" with "real-time." True real-time AI is rarely necessary for MVPs
User perception trumps technical purity - A 2-second response that feels instant beats a 100ms response that feels broken
Progressive loading is your friend - Immediate acknowledgment plus progressive reveal creates the illusion of real-time processing
Pre-loading beats optimization - Caching predictable responses delivers better performance than optimizing API calls
Bubble's constraints can be features - Working within platform limitations often leads to more robust, maintainable solutions
Hybrid approaches work - Combining instant responses with background AI processing satisfies user expectations
Fallbacks prevent frustration - Always have a plan for when AI fails or takes too long
If I were building this again, I'd start with user journey mapping instead of technical architecture. Understanding what users actually need from "real-time" AI would have saved weeks of prototyping and led to the hybrid solution immediately.
The biggest mistake founders make is assuming their users want the same real-time experience as consumer chat apps. Business users have different expectations and tolerance levels - use this to your advantage.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups considering AI features in Bubble:
Start with user interviews to validate real-time requirements
Implement progressive loading for all AI interactions
Build a content caching system for common AI responses
Plan fallback workflows for AI failures or delays
For your Ecommerce store
For ecommerce stores implementing AI features:
Pre-generate product recommendations during off-peak hours
Use progressive loading for AI-powered search results
Cache personalized content based on user behavior patterns
Implement smart fallbacks to bestsellers when AI fails