Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last year, I had a potential client approach me with an exciting opportunity: build a two-sided marketplace platform with AI features. The budget was substantial, the technical challenge was interesting, and it would have been one of my biggest projects to date.
I said no.
Here's why – and what this taught me about the real purpose of AI prototypes in 2025. While everyone's talking about complex AI development and expensive custom builds, I've discovered something that completely changed how I approach AI-powered prototyping.
The no-code revolution, combined with AI capabilities, has created an unprecedented opportunity. You can now build sophisticated AI prototypes in days, not months. But here's what most people get wrong: they're still thinking like it's 2020.
In this playbook, you'll learn:
Why I turned down a $XX,XXX AI platform project and what I built instead
My exact framework for building AI prototypes with Bubble.io in under a week
The 3-layer validation system that saves months of development time
Real examples from my client work and the metrics that prove this approach works
When to prototype fast vs when to build custom (most people get this backwards)
This isn't theory. This is what I've learned from actually building AI prototypes for startups who needed to validate ideas quickly, not waste months on custom development. Check out our other insights on SaaS development strategies and AI implementation for more context.
Industry Reality
What the No-Code + AI World Actually Promises
If you're exploring AI prototyping right now, you've probably been told that no-code platforms like Bubble.io can help you build "anything quickly and cheaply." The marketing messaging is everywhere: "Build AI apps without coding!" "Launch in days, not months!" "No technical skills required!"
Here's what the industry typically recommends for AI prototyping:
Use no-code platforms - Bubble, Webflow, or similar drag-and-drop builders
Integrate AI APIs - Connect ChatGPT, Claude, or custom models through simple integrations
Focus on MVP features - Build minimal viable products to test core concepts
Iterate quickly - Use the speed advantage to test and improve rapidly
Scale when proven - Move to custom development only after validation
This conventional wisdom exists because it sounds logical. No-code platforms have democratized development, AI APIs have become accessible, and the startup world loves the idea of rapid iteration. The promise is compelling: validate ideas fast and cheap.
But here's where this approach falls short in practice. Most founders and agencies treat AI prototyping like they're building a finished product. They get caught up in features, UI polish, and technical complexity when they should be focused on one thing: validation.
I've seen too many teams spend three months building a "prototype" that could have tested the core assumption in three days. The tools are powerful, but the strategy is often wrong. That's where my different approach comes in.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The client came to me excited about the no-code revolution and new AI tools. They'd heard these tools could build anything quickly and cheaply. They weren't wrong – technically, you can build a complex AI-powered platform with these tools.
But their core statement revealed the problem: "We want to see if our idea is worth pursuing."
They had no existing audience, no validated customer base, no proof of demand. Just an idea and enthusiasm. This is where most AI prototyping projects go wrong – they confuse building with validating.
Here's what I told them that initially shocked them: "If you're truly testing market demand, your AI prototype should take one day to build, not three months."
The real issue wasn't technical capability. Bubble.io could absolutely handle their marketplace concept. AI integrations through APIs like OpenAI are straightforward. The problem was strategic: they wanted to build first, validate second.
I've worked on enough SaaS projects to know that this approach leads to beautiful products that nobody uses. Even with AI and no-code tools, building a functional two-sided platform takes significant time. But if you're truly testing market demand, your first MVP shouldn't be a product at all.
Instead of taking their money to build something impressive, I recommended they start with manual validation. Create a simple landing page explaining the AI-powered solution, start manual outreach to both sides of their marketplace, and manually match supply and demand. Only after proving demand should they consider building automation.
This experience reinforced a principle I now share with every client considering AI prototyping: In the age of AI and no-code, the constraint isn't building – it's knowing what to build and for whom.
Here's my playbook
What I ended up doing and the results.
After walking away from that project, I developed a framework that I now use for every AI prototyping engagement. This isn't about the tools – it's about the process. Here's my exact approach:
Layer 1: Concept Validation (Day 1)
Before touching Bubble.io or any development tool, I create what I call a "smoke test prototype." This is literally a landing page or Notion document that explains the AI-powered value proposition. No backend, no AI integration, just a clear explanation of what the solution would do.
For one client in the HR space, we created a simple page describing an AI-powered interview scheduler. Instead of building the AI, we manually scheduled interviews for the first 10 companies that signed up. This took one day to set up and immediately showed us demand existed.
Layer 2: Manual Process Validation (Week 1)
Once we have initial interest, I design the manual version of what the AI would eventually automate. This is where most people skip ahead to building, but this step is crucial.
Using the HR example, we spent a week manually doing what the AI would do: parsing resumes, matching candidates to requirements, and scheduling interviews. We used Google Sheets, email templates, and Calendly. No code, no AI APIs – just the core value proposition delivered manually.
The insights from this week were invaluable. We learned that companies cared more about candidate quality than speed. We discovered that the real bottleneck wasn't scheduling – it was the initial screening. These insights completely changed what we eventually built in Bubble.io.
Layer 3: Technical Prototype (Week 2-3)
Only after manual validation do I open Bubble.io. But here's the key: I'm not building a complete product. I'm automating the specific parts of the manual process that showed the most value.
For our HR client, we built a simple Bubble app that:
Collected resume uploads through a simple form
Used OpenAI's API to extract key qualifications
Automatically scored candidates against job requirements
Sent the top matches to hiring managers for review
Notice what we didn't build: user authentication, payment processing, advanced scheduling, company dashboards. We automated the core value – candidate screening – and kept everything else manual.
This Bubble prototype took 5 days to build and immediately showed whether the AI automation was worth the complexity. In this case, it was. The AI screening saved hiring managers 3 hours per open position, and they were willing to pay $200/month for that time savings.
Bubble Setup
Start with data structure and core workflows, not UI design
API Integration
Use Bubble's API Connector for AI services - test endpoints first
Validation Focus
Build only what proves the core value proposition, not full features
User Testing
Get 5-10 real users interacting with the prototype within 48 hours
The results from this approach consistently surprise clients. Instead of spending 3 months building something impressive that might not work, we typically have market validation and paying customers within 3 weeks.
For the HR client, we had 12 companies paying $200/month within 30 days of launching the Bubble prototype. Total development time: 2 weeks. Total validation time: 1 month.
Compare this to another project where a client insisted on building the "full vision" first. They spent 6 months and $50K building a comprehensive AI platform. Launch day: zero customers. The market didn't want what they built, but they only discovered this after massive investment.
The timeline difference is dramatic:
Traditional approach: 6 months to market feedback
My framework: 1 week to market feedback
But the real value isn't speed – it's learning. By the time traditional projects get market feedback, they're too invested to pivot. With this framework, pivoting is cheap and fast. We can test 3-4 different AI prototype concepts in the time it takes to build one traditional MVP.
The Bubble prototypes also serve as perfect technical specifications for eventual custom development. When clients do decide to build custom solutions, the prototype becomes the blueprint. No guesswork, no assumptions – just validated features that customers already pay for.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After using this framework across multiple AI prototype projects, here are the essential lessons I've learned:
Manual validation first, automation second. Every hour spent manually delivering your AI solution's value is worth 10 hours of development time. You'll learn what actually matters to customers.
Bubble.io is perfect for AI prototypes, terrible for complex products. Use it to prove concepts, not to build scalable solutions. The constraint is strategic, not technical.
AI APIs are commodities; workflow design is valuable. OpenAI, Claude, and similar services are tools. The magic is in how you connect them to solve real problems.
Users care about outcomes, not AI. Nobody wakes up wanting "AI-powered" anything. They want better, faster, cheaper solutions to real problems.
Prototype fatigue is real. If your prototype takes more than 2 weeks to build, you're building a product, not a prototype. Scale back ruthlessly.
The best prototypes break after 100 users. If it's robust enough to handle thousands of users, you probably over-engineered it for the validation phase.
Document everything. These prototypes become invaluable specifications for eventual custom development or platform migration.
This approach works best when you have a clear hypothesis about user behavior and are willing to start simple. It doesn't work when stakeholders are attached to specific features or when you're trying to impress investors with technical complexity.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups building AI features:
Start with manual validation of AI workflows before building
Use Bubble prototypes to test core value props with real users
Focus on single AI use case rather than comprehensive platform
Plan 2-week prototype cycles for rapid iteration and learning
For your Ecommerce store
For ecommerce businesses exploring AI automation:
Test AI recommendations manually before building recommendation engines
Prototype AI customer service with existing tools first
Use simple Bubble forms to test AI-powered product suggestions
Validate AI personalization impact on small customer segments