Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Six months ago, every client conversation started the same way: "Can we integrate ChatGPT into our Bubble MVP?" They wanted AI-powered everything—chatbots, content generators, automated responses. The technology was there, Bubble made it accessible, and everyone was convinced that AI was the magic ingredient their startup needed.
Then I had my wake-up call. After helping multiple clients build AI integrations that nobody used, I realized we were solving the wrong problem. We were building AI features before validating that anyone wanted our core product.
Here's what changed my entire approach: I started deliberately avoiding AI integrations in early MVPs. Counterintuitive? Absolutely. But this strategy led to faster validation, lower development costs, and better product-market fit for my clients.
Through working with B2B SaaS clients and implementing AI automation workflows, I learned that most founders are asking the wrong question. Instead of "How can we integrate ChatGPT?" they should be asking "What problem are we actually solving?"
Here's what you'll discover:
Why ChatGPT integration can kill your MVP's learning potential
My manual-first validation framework that proves AI demand
When to integrate ChatGPT vs. when to fake it completely
The specific Bubble workflow I use when AI integration makes sense
How to structure your MVP for easy AI migration later
This approach has saved clients months of development time and thousands in API costs—while helping them build products people actually want.
Current Hype
What Every AI Startup Guide Tells You
Open any AI startup guide in 2025 and you'll see the same playbook repeated everywhere:
Start with AI integration - Use ChatGPT API, Claude, or other language models as your core feature
Build on platforms like Bubble - Leverage no-code tools to rapidly prototype AI functionality
Focus on user experience - Create seamless interfaces that hide the AI complexity
Iterate based on AI performance - Improve prompts, fine-tune models, optimize responses
Scale with AI infrastructure - Build robust systems that can handle growing AI API usage
This conventional wisdom exists because it sounds logical. AI is the future, no-code platforms make integration accessible, and everyone wants to be "AI-first." The promise is compelling: build intelligent products faster than ever before.
But here's where this approach falls short: it optimizes for building cool AI features instead of solving real customer problems.
I've watched startups spend months perfecting their ChatGPT prompts when their core value proposition was still unproven. They treat AI integration like a product requirement instead of what it actually is—an expensive assumption that needs validation.
The real constraint in successful MVP development isn't technical capability—it's understanding what customers actually need.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The project that changed everything was a B2B SaaS client who wanted to build an "AI-powered content generator" using Bubble and ChatGPT integration. They had the technical specs mapped out, API costs calculated, and user flows designed. It would have been a complex but impressive build.
But during our discovery call, I asked a simple question: "Who specifically will pay for this, and how do you know they want it?"
Silence.
They had an idea, enthusiasm for AI technology, and assumptions about market demand. What they didn't have was proof that anyone would actually use their AI-powered solution over existing alternatives.
This is when I learned my most important lesson about AI MVPs: the constraint isn't building AI functionality—it's proving people want what you're building in the first place.
Instead of immediately starting the Bubble development, I recommended something that initially frustrated them: manual validation. For one month, they would manually provide the "AI-generated" content themselves, using their own expertise instead of ChatGPT.
The results were eye-opening. Within two weeks, we discovered that their target customers didn't want automated content generation—they wanted content strategy guidance. The AI integration they'd planned would have solved the wrong problem entirely.
This experience taught me that in the age of accessible AI tools, the real skill isn't technical integration—it's knowing when NOT to integrate AI yet.
Here's my playbook
What I ended up doing and the results.
Based on this experience and working with multiple clients on AI projects, I developed what I call the "Manual-First AI Framework." It's designed to prove AI demand before you build AI functionality.
Phase 1: Manual Validation (Week 1-2)
Before any Bubble development, manually deliver what your AI would theoretically do. If you're building a ChatGPT-powered writing assistant, write the content yourself. If it's an AI customer service bot, handle inquiries manually.
This isn't about scalability—it's about learning. You'll discover what customers actually want, which prompts work, and what responses create value. More importantly, you'll learn if people are willing to pay for the outcome, regardless of how it's produced.
Phase 2: Wizard of Oz MVP (Week 3-4)
Once you've proven manual demand, build the interface in Bubble without the AI backend. Create forms, dashboards, and user flows that look like they're AI-powered but are actually human-powered behind the scenes.
This is where Bubble shines. You can build sophisticated interfaces quickly, collect real user data, and maintain the illusion of automation while still learning what works. Your "AI responses" are actually your carefully crafted manual responses.
Phase 3: Selective AI Integration (Week 5-8)
Only after proving demand and understanding user expectations do you start integrating actual AI. Begin with the most repetitive, well-understood tasks. Use ChatGPT API for scenarios where you've already proven the manual approach works consistently.
In Bubble, this means building your first real AI workflows—API calls to OpenAI, response processing, and error handling. But you're doing this with validated prompts and proven use cases, not educated guesses.
Phase 4: Hybrid Optimization (Week 9-12)
The final phase combines AI automation with human oversight. Your Bubble app uses ChatGPT for initial processing but includes manual review workflows for quality control. This gives you the speed of AI with the reliability your early customers need.
Throughout this process, you're building toward full automation—but only after proving each step works independently.
Validation Framework
Testing demand before building AI functionality
Manual approaches that reveal what customers actually value
Implementation Strategy
Building Bubble interfaces that work with or without AI backend
Technical Integration
Smart API architecture that doesn't lock you into specific AI providers
Quality Control
The client who originally wanted the AI content generator ended up building something completely different—and more valuable. Instead of automated content creation, they built a content strategy tool that combined AI research with human expertise.
Their final product used ChatGPT for research and data gathering, but the strategic insights came from their team. The Bubble interface made this hybrid approach feel seamless to users, while the business model was based on high-value human expertise rather than commodity AI output.
Key metrics from this approach:
Time to first paying customer: 3 weeks (vs. estimated 3 months with full AI build)
Development costs: 70% lower than original AI-first plan
Customer retention: 85% vs. industry average of 60% for similar tools
Monthly recurring revenue: $15k within 90 days
More importantly, they built something customers actually wanted instead of something technically impressive but commercially useless.
I've since applied this framework to other AI project consultations, consistently finding that manual validation reveals customer needs that pure AI solutions miss. The technology becomes a tool for enhancing proven value, not creating hypothetical value.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
AI amplifies existing problems - If your manual process doesn't work, AI won't fix it. It will just create bad results faster.
Customers pay for outcomes, not methods - Nobody cares if you use ChatGPT or human expertise. They care about getting their problem solved effectively.
API costs add up quickly - ChatGPT integration seems cheap until you have active users. Budget for 5-10x more API usage than your estimates.
Bubble makes iteration easy - The platform's strength isn't AI integration—it's rapid interface changes. Use this for learning, not just building.
Manual insights beat AI optimization - Time spent understanding customer problems manually is more valuable than time spent optimizing prompts.
Hybrid approaches work best - Pure AI solutions often fail. AI + human oversight creates reliability that early customers need.
Distribution matters more than AI - The best ChatGPT integration won't save a product nobody knows about. Focus on distribution strategy first.
The biggest insight? In 2025, AI literacy isn't about technical integration skills—it's about understanding when AI solves real problems vs. when it's just impressive technology.
Most successful AI startups I now work with spend more time on customer development than on prompt engineering. They use AI to enhance proven processes, not to guess what customers might want.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups considering ChatGPT integration:
Start with manual customer service before building AI chat
Use Bubble's interface tools to fake AI functionality during validation
Build hybrid workflows that combine AI speed with human oversight
Focus on API cost management and response time optimization
Create fallback systems for when AI responses need human review
For your Ecommerce store
For e-commerce stores exploring ChatGPT features:
Test AI-powered product recommendations manually before automation
Use ChatGPT for customer inquiry responses only after training on your data
Integrate AI search functionality once you understand search patterns
Build AI content generation for product descriptions after manual testing
Implement AI-powered sizing or recommendation tools with human verification