Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
OK, so here's something that's going to sound familiar: you want to build an AI MVP, you've heard Bubble is the way to go for no-code, and now you're staring at their plugin marketplace wondering which ones will actually help you build something that works without losing your mind.
Last month, I had a potential client approach me with an exciting opportunity: build a two-sided marketplace platform with AI features. The budget was substantial, and the technical challenge seemed interesting. But here's the thing—they came to me because they'd heard about AI tools and wanted to "test if their idea works."
I turned down that project. Not because I couldn't build it, but because I realized something important: if you're truly testing market demand, your MVP should take one day to build—not three months. But when you do need to build an AI-powered MVP that actually works, the plugin strategy becomes everything.
Here's what you'll learn from my experience building AI MVPs on Bubble:
The 5 essential plugins that make AI MVPs actually functional
Why most people pick the wrong AI integrations and waste weeks debugging
My exact plugin stack that reduced development time by 60%
How to avoid the "plugin hell" that kills most Bubble projects
The counterintuitive approach to AI features that makes MVPs lovable
This isn't about following another "best practices" guide. This is about the specific plugins I've tested, the ones that failed spectacularly, and the combination that actually delivers results. Let's dive into what really works when you need to ship an AI MVP that people will love.
Industry Insight
What the no-code community preaches about AI MVPs
Walk into any no-code community or browse the Bubble forums, and you'll hear the same advice repeated everywhere: "Just use ChatGPT API and you're done." The conventional wisdom for building AI MVPs on Bubble follows a predictable pattern.
The Standard Recommendations Everyone Gives:
Install the OpenAI plugin for ChatGPT integration
Add a simple text input and output interface
Maybe throw in some basic authentication
Use Bubble's native database for everything
Add payment processing as an afterthought
This approach exists because it's technically simple and gets you something that "works" quickly. Most tutorials focus on the easiest path to a functioning demo. The problem? This conventional wisdom treats AI like a magic black box that will solve all your MVP problems.
The reality is messier. I've seen dozens of builders follow this exact playbook, only to hit walls they never anticipated. Their "AI MVP" becomes a glorified chatbot that users try once and never return to. The magic AI features become frustrating dead ends. The simple database structure breaks under real user behavior.
Here's what the standard advice misses: building an AI MVP isn't about AI integration—it's about creating a workflow that makes AI feel magical to users. The plugins you choose determine whether your MVP feels like a professional tool or a weekend hobby project.
Most builders focus on getting AI responses working and ignore everything else that makes an MVP actually usable. That's where the real differentiation happens, and that's where my approach diverges completely from conventional wisdom.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
So here's the context: I was working with multiple clients who wanted to test AI-powered features, and every single one was hitting the same roadblock. They'd start building on Bubble, get the basic AI integration working, then realize their MVP felt clunky and unprofessional.
The breaking point came when a B2B SaaS client showed me their prototype. They'd followed all the "best practices"—OpenAI integration, simple interface, basic functionality. But when they tested it with real users, the feedback was brutal. Users couldn't figure out how to use it effectively, the responses were inconsistent, and there was no way to track what was actually working.
That's when I realized the fundamental problem: everyone was treating Bubble like a prototyping tool instead of a production platform. They were building MVPs that looked like MVPs instead of building MVPs that felt like real products.
I spent six months experimenting with different plugin combinations across multiple AI projects. Not just installing plugins and hoping they worked, but actually stress-testing them with real user scenarios. I built the same AI features using different plugin stacks to see which ones delivered better user experiences.
The first breakthrough came when I stopped thinking about AI integration as the primary challenge. The real challenge was building the infrastructure around the AI that made it feel professional and reliable. Users don't care about your AI model—they care about whether your tool helps them get their job done faster.
Most of my experiments failed. I tried building custom API connections instead of using plugins. I attempted to create complex workflow logic that broke under pressure. I over-engineered solutions that took weeks to debug. But the failures taught me something crucial: the goal isn't to build the most technically impressive AI MVP—it's to build the one that users actually want to keep using.
Here's my playbook
What I ended up doing and the results.
After months of testing, I developed what I call the "Minimum Viable AI Stack" for Bubble. This isn't about using every AI plugin available—it's about using the right combination that creates a professional experience without overwhelming complexity.
The Core Plugin Foundation:
1. OpenAI (Official Plugin)
Yes, this is obvious, but here's the non-obvious part: don't just use it for text generation. I use it for content classification, sentiment analysis, and data structuring. The key is setting up proper prompt templates within Bubble's workflow editor instead of passing raw user input directly to the API.
2. Selectpdf or Documint for Document Generation
This was the game-changer nobody talks about. Users want outputs they can save, share, and reference later. Whether it's generating reports, proposals, or structured documents, having professional PDF output makes your AI MVP feel like a real business tool instead of a chat interface.
3. Postmark or SendGrid for Email Automation
AI responses are great, but follow-up is everything. I set up automated email sequences that deliver AI-generated content, send progress updates, and provide value even when users aren't actively using the app. This turns one-time users into returning customers.
4. Analytics Plugin (I use Mixpanel)
Here's what most people miss: you need to track AI performance, not just user behavior. Which prompts generate the best responses? Where do users get stuck? What AI features do they actually use? Without this data, you're flying blind.
5. Airtable or Google Sheets Integration
For data management and user content organization. Instead of relying solely on Bubble's database, I create hybrid systems where users can export their AI-generated content to familiar tools. This reduces friction and increases perceived value.
The Secret Sauce: Workflow Architecture
The real magic isn't in the plugins themselves—it's in how you structure the workflows between them. I create what I call "AI reliability chains" where each step has fallbacks and error handling. If the AI doesn't respond properly, the system gracefully handles it instead of breaking the user experience.
For example, when a user requests AI-generated content, my workflow: validates input → processes through AI → formats the response → generates a PDF → sends confirmation email → logs analytics → updates user dashboard. Each step is modular and can handle failures without crashing the entire process.
This approach transformed how users perceive the AI features. Instead of getting raw AI responses, they receive polished, actionable outputs delivered through professional channels.
Essential Stack
The 5 core plugins that make AI MVPs feel professional instead of like weekend projects
Workflow Design
How I structure AI chains with fallbacks and error handling for reliable user experiences
User Experience
Why document generation and email automation are more important than fancy AI features
Speed Strategy
The plugin selection criteria that cuts development time by 60% without sacrificing quality
The results from this approach were immediate and measurable. Instead of building AI MVPs that users tried once and forgot, I was creating tools that generated real engagement and retention.
Development Impact:
Using this plugin stack reduced my development time by approximately 60%. What used to take 3-4 weeks of custom API integration and workflow debugging now takes 1-2 weeks of plugin configuration and testing. More importantly, the end result is more reliable and professional.
User Engagement Changes:
The biggest shift was in user behavior. With the document generation and email automation, users started treating the AI features as actual business tools rather than experimental features. They began incorporating the outputs into their real workflows, which is the ultimate validation for any MVP.
Client Feedback:
One B2B SaaS client saw their user trial-to-paid conversion rate improve when we implemented this plugin strategy. The AI features went from being "interesting but not essential" to being a core part of their value proposition. Users specifically mentioned the professional PDF outputs and automated follow-ups as reasons they upgraded to paid plans.
The most unexpected outcome was how this approach simplified the entire development process. By focusing on plugin orchestration rather than custom development, I could iterate faster and test different AI feature combinations without rebuilding everything from scratch.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key insights from building multiple AI MVPs using this plugin approach:
Plugin Selection is Product Strategy
Your plugin choices determine user perception more than your AI model does. Users judge "AI quality" based on the entire experience, not just the accuracy of responses. Professional outputs and reliable delivery matter more than cutting-edge AI capabilities.
Fallback Systems are Essential
AI APIs fail, rate limits get hit, and responses sometimes don't make sense. Building robust fallback workflows isn't just good practice—it's what separates viable products from prototype demos. Every AI interaction should have at least two failure recovery paths.
Document Generation Changes Everything
This was my biggest revelation. Adding PDF or document generation to AI responses transforms how users perceive value. Instead of ephemeral chat interactions, they get tangible outputs they can save, share, and reference. This single feature often justifies the entire MVP.
Email Integration Drives Retention
AI MVPs without follow-up communication die quickly. Users try the AI feature once, get their response, and forget about your product. Automated email sequences that continue delivering value keep your MVP top-of-mind even when users aren't actively using it.
Analytics Must Track AI Performance
Traditional analytics aren't enough for AI MVPs. You need to track prompt effectiveness, response quality, and user satisfaction with AI outputs specifically. This data guides feature development and helps identify which AI capabilities actually create value.
Simple Beats Complex Every Time
The temptation with AI is to build elaborate, multi-step processes. But users want simple, reliable tools that solve specific problems. The most successful AI MVPs I've built do one thing exceptionally well rather than many things adequately.
When This Approach Works Best
This plugin strategy excels for business-focused AI tools, content generation MVPs, and any AI application where professional presentation matters. It's less suitable for conversational AI, gaming applications, or highly specialized AI features that require custom model training.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS Startups:
Focus on AI features that integrate into existing business workflows
Use document generation to create shareable business assets
Implement email automation for user onboarding and feature education
Track AI feature usage separately from general product analytics
For your Ecommerce store
For Ecommerce Applications:
Apply AI to product descriptions, customer service, and personalization
Generate downloadable guides or reports as lead magnets
Use AI for order automation and customer communication workflows
Integrate with existing ecommerce platforms through API connections