Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last year, a potential client approached me with what seemed like the perfect project: build a two-sided marketplace platform with a substantial budget. I said no. Not because of the money, but because they wanted to "test if their idea works" by building a complex AI-powered platform.
This is the reality most founders face today. You've got this brilliant AI MVP idea, you've heard Bubble can build anything, and you're wondering which plugins will make your vision reality. But here's what I learned after saying no to that $XX,XXX project: if you're truly testing market demand, your MVP should take one day to build, not three months.
The problem isn't finding the right Bubble plugins for your AI MVP. The problem is that most founders are treating AI like magic and MVPs like final products. I've watched too many startups burn through budgets building "minimum viable" platforms that are anything but minimal.
In this playbook, you'll discover:
Why the best AI MVP plugins aren't always the most popular ones
The exact plugin stack I recommend for AI MVP validation
How to choose plugins that scale without breaking your budget
When to avoid plugins entirely and go custom
The validation-first approach that saves months of development
Market Reality
What every founder thinks about AI MVPs
Walk into any startup accelerator today and you'll hear the same advice repeated like gospel: "Build fast, fail fast, iterate faster." When it comes to AI MVPs on Bubble, the conventional wisdom goes something like this:
Use OpenAI's API plugin - It's the most popular, so it must be the best
Add as many AI features as possible - More AI = more impressive to investors
Build everything in Bubble - No-code means faster development
Launch with a complete platform - Users expect polished experiences
Scale plugins as you grow - Start small, upgrade later
This advice exists because it sounds logical. Use the biggest name (OpenAI), leverage the hottest trend (AI), build on the fastest platform (Bubble), and ship something impressive. VCs love AI, no-code is democratizing development, and everyone's rushing to market.
The problem? This approach treats your MVP like a product launch instead of a learning experiment. You end up with founders spending 3-6 months building AI-powered platforms with sophisticated plugin architectures, beautiful UIs, and zero validated demand.
I've seen this pattern repeated dozens of times: brilliant technical execution solving problems that don't actually exist. The issue isn't the plugins - it's the assumption that building faster means learning faster.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Here's the situation I encounter constantly: founders come to me with spreadsheets comparing Bubble AI plugins, asking which combination will bring their vision to life fastest. They've usually spent weeks researching the perfect tech stack - OpenAI vs Claude vs custom APIs, whether to use Bubble's native AI features or third-party plugins, how to handle data processing at scale.
One particular conversation stands out. A founder had mapped out their entire AI MVP: user authentication, AI conversation flows, data analytics, payment processing, notification systems. They wanted to build a "simple" AI coaching platform and had identified 8 different plugins they'd need. Their timeline? Three months to launch.
The problem wasn't their plugin selection - they'd done solid research. The problem was they had no existing audience, no validated customer base, and no proof anyone wanted AI coaching. They were essentially asking me to help them build a sophisticated solution for a problem they assumed existed.
This is when I started pushing back on these projects. Not because the technical execution would be difficult, but because the entire premise was backwards. They were optimizing for building speed when they should have been optimizing for learning speed.
What frustrated me most was watching founders get caught up in plugin comparison paralysis. Should they use the official OpenAI plugin or a third-party alternative? Does the ChatGPT plugin offer better customization than the Claude integration? Will their chosen plugin handle the scale they're imagining?
These are the wrong questions entirely. The right question is: how do you validate demand for your AI solution before building anything at all?
Here's my playbook
What I ended up doing and the results.
Instead of helping that founder build their AI coaching platform, I recommended something that made them uncomfortable: test demand manually first, then build the minimum viable automation.
Here's the exact process I now recommend for any AI MVP on Bubble:
Phase 1: Manual Validation (Week 1)
Before touching any plugins, prove demand exists. Create a simple landing page explaining your AI solution's value proposition. Drive traffic through content, outreach, or small paid campaigns. The goal isn't to build AI - it's to see if people want what you're promising.
For the coaching founder, this meant creating a "AI-powered coaching" landing page and manually delivering coaching sessions to early users. No automation, no plugins, just human-delivered value that felt like AI to the user.
Phase 2: Plugin Selection Strategy
Once you've validated demand, choose plugins based on validated use cases, not features. Here's my current recommended stack:
OpenAI API Plugin (Official) - Most reliable for text generation and conversation
Bubble's Native API Connector - For custom AI service integration when you need more control
Zapier Plugin - Essential for connecting AI workflows to external tools
Database Triggers - For automating AI responses based on user actions
Phase 3: Progressive AI Implementation
Start with the simplest possible AI integration. For most MVPs, this means one core AI function - maybe text generation, or simple Q&A, or basic content analysis. Resist the urge to build comprehensive AI capabilities immediately.
The coaching founder's first AI integration was a simple prompt-response system using the OpenAI plugin. Users submitted questions, the AI generated coaching advice, and a human (the founder) reviewed responses before sending. This hybrid approach let them test AI quality while maintaining control.
Phase 4: Scale Based on Real Usage
Only add complexity after seeing how users actually interact with your basic AI features. Most founders assume they need sophisticated conversational AI, but users often prefer simple, reliable AI tools over complex, unpredictable ones.
Core Strategy
Start with manual validation, then add the minimal AI automation needed to prove your concept works.
Plugin Priority
Choose reliability over features. The official OpenAI plugin beats fancy alternatives that might break your MVP.
Integration Approach
Hybrid human-AI systems let you test AI capabilities while maintaining quality control during validation.
Scaling Philosophy
Add AI complexity only after validating that users want and use your basic AI features consistently.
The results of this approach consistently surprise founders who expect building to be the hard part. For the coaching founder, manual validation took two weeks and cost under $500 in advertising. They discovered users wanted AI-generated action plans, not conversational coaching.
This insight completely changed their product direction. Instead of building a chat-based AI coach, they built a simple action plan generator. One OpenAI API call, basic Bubble logic, human review. The entire "AI MVP" was built in three days.
More importantly, they had paying customers before they wrote a single line of code. The landing page generated 47 email signups and 12 people who paid $97 for manual coaching sessions. This revenue funded their actual MVP development.
The pattern holds across different AI MVP ideas. Validation reveals what users actually want from AI, which is usually much simpler than what founders imagine. Most successful AI MVPs use 1-2 plugins maximum, not the comprehensive stacks founders plan.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons learned from applying this validation-first approach to AI MVPs:
Distribution beats AI sophistication - Users care more about finding your solution than how advanced your AI is
Simple AI with human oversight beats complex autonomous AI - Hybrid systems are more reliable and trustworthy
Plugin selection doesn't determine success - The specific plugins matter far less than validating real demand first
Manual delivery teaches you what to automate - You can't build good AI without understanding the manual process
Users want reliable AI tools, not impressive AI demos - Consistency beats sophistication for MVPs
Revenue validation changes everything - Paying customers reveal what AI features actually matter
Technical complexity is rarely the constraint - Finding product-market fit is always harder than building the product
The biggest mindset shift: stop thinking about AI MVPs as technical challenges and start thinking about them as distribution challenges. The AI is usually the easy part.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups building AI MVPs:
Start with manual delivery to validate your AI value proposition
Use OpenAI API plugin for reliable text generation capabilities
Implement hybrid human-AI workflows during validation phase
Scale AI complexity only after proving basic demand exists
For your Ecommerce store
For ecommerce stores exploring AI features:
Focus on one AI application: product recommendations, customer service, or content generation
Test AI features manually before automating with Bubble plugins
Use Zapier integration for connecting AI to existing ecommerce workflows
Prioritize reliability over advanced AI capabilities for customer-facing features