Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last month, a potential client approached me with an exciting opportunity: build a comprehensive two-sided marketplace platform. 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 the thing - they 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, 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. And that's exactly when you shouldn't be building complex platforms - that's when you should be building something lovable, not just viable.
In this playbook, you'll learn:
Why the traditional MVP approach fails in the age of AI tools
My framework for creating lovable prototypes that actually validate demand
How to turn product validation into your marketing strategy
The specific tools and workflows that make lovable prototyping possible
Real examples of prototypes that became successful products vs. those that saved founders from expensive mistakes
This isn't about building faster or cheaper - it's about building smarter. AI-powered prototyping has fundamentally changed what's possible in the validation phase, and most founders are still thinking like it's 2019.
Industry Reality
What every startup founder has already heard
Walk into any startup accelerator or read any lean startup guide, and you'll hear the same advice: "Build a Minimum Viable Product (MVP) to test your assumptions." The conventional wisdom goes like this:
Start with core features only - Strip everything down to the bare minimum functionality
Ship fast and iterate - Get something out in weeks, not months
Measure user behavior - Track metrics to validate product-market fit
Pivot based on feedback - Use data to inform your next iteration
Scale what works - Double down on validated features
This framework made perfect sense when building software was expensive and time-consuming. Back then, the constraint was development resources, so you had to be ruthlessly minimal. The "viable" part was about finding the smallest thing that could technically function.
But here's where this approach breaks down in 2025: When building becomes cheap and fast, "minimum" and "viable" become the wrong constraints. With AI tools and no-code platforms, you can build a functional MVP in days, not months. The bottleneck isn't development anymore - it's attention and emotional engagement.
Think about your own behavior. How many "viable" products have you tried and immediately forgotten? How many minimal apps have you downloaded, used once, and never opened again? The problem isn't that they didn't work - it's that they didn't make you care.
Most MVPs fail not because they're technically insufficient, but because they're emotionally forgettable. They solve problems people have, but they don't create experiences people want. And in a world where anyone can build an MVP in a weekend, that's the difference between success and obscurity.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
This shift in my thinking came from a painful realization during several SaaS client projects where I'd built technically sound MVPs that nobody cared about. The products worked perfectly, the user flows were optimized, the features were well-designed - but engagement was terrible.
The breakthrough happened when I started analyzing what made some prototypes stick while others disappeared. It wasn't the feature set or the technical implementation. The products that succeeded had something different: they made people feel something.
Take one particular client - a B2B SaaS startup targeting small agencies. We built a clean, functional project management tool. All the core features were there: task management, time tracking, client collaboration. It was a textbook MVP. The result? Users would sign up, poke around for a few minutes, and never return.
The problem wasn't what the product did - it was how it made people feel. It felt like every other project management tool they'd tried. There was no emotional hook, no moment of delight, no reason to tell their colleagues about it.
That's when I realized we'd been optimizing for the wrong thing. Instead of asking "What's the minimum we can build?" we should have been asking "What's the minimum we can build that people will actually love?"
This led me to study products that achieved rapid adoption despite being early-stage. Companies like Notion, Linear, and even early Twitter - they weren't just functional, they were delightful. They created emotional connections that made users want to share them, even when they were clearly incomplete.
The pattern became clear: in the age of infinite choice and short attention spans, lovability isn't a nice-to-have feature - it's the primary validation metric. If people don't love your prototype enough to tell others about it, they won't love your finished product either.
This realization completely changed my approach to early-stage product development and client consulting. Instead of building minimal viable products, I started building lovable prototypes - products designed to create emotional engagement first, comprehensive functionality second.
Here's my playbook
What I ended up doing and the results.
Here's the framework I've developed for creating lovable AI app prototypes that actually validate demand and create genuine user excitement:
Step 1: Design for Emotion First, Function Second
Traditional MVP thinking starts with user stories and feature requirements. Lovable prototyping starts with emotional outcomes. Before writing a single line of code, I map out the specific emotions I want users to feel at each stage of their interaction.
For AI apps specifically, this means focusing on moments of "magic" - those instances where the AI does something that feels almost supernatural. Maybe it's a recommendation that's eerily perfect, or an automation that saves exactly the right amount of time, or insights that feel like mind-reading.
I use a simple framework: Surprise → Delight → Pride → Sharing. Every interaction should surprise users with how well it works, delight them with how it feels, make them proud of what they've accomplished, and compel them to share it with others.
Step 2: Build the "Demo Magic" First
Instead of building comprehensive functionality, I start by creating the single most impressive 30-second experience possible. This is what I call "demo magic" - the one interaction that makes someone go "wow" and immediately want to show it to a colleague.
For AI prototypes, this often means:
Creating an onboarding experience that generates immediate, personalized value
Focusing on one AI capability that feels genuinely intelligent, not just automated
Designing for the "I have to show this to someone" moment
Step 3: Use AI to Create Personalized Experiences at Scale
Here's where AI prototypes have a massive advantage over traditional MVPs: you can create personalized experiences without building complex user management systems. I leverage AI to make every interaction feel custom-tailored, even in a prototype environment.
The key is using AI not just as a feature, but as the personalization engine that makes each user feel like the product was built specifically for them. This might mean:
AI-generated onboarding flows based on user responses
Dynamic interface adaptation using large language models
Contextual AI assistance that learns from user behavior in real-time
Step 4: Implement "Progressive Revelation" UX
Instead of showing all features upfront, I design prototypes that reveal capabilities gradually as users engage more deeply. This creates a sense of discovery and keeps the initial experience simple while hinting at deeper functionality.
The pattern looks like this: Simple first interaction → Impressive result → Hint at more capabilities → Guided discovery → Advanced features unlock naturally.
Step 5: Build Virality Into the Core Experience
Rather than adding sharing features as an afterthought, I design the core product experience to naturally create shareable moments. For AI apps, this often means:
Generating outputs that users want to show off
Creating "before and after" moments that demonstrate clear value
Building collaborative features that require users to invite others
The goal is making sharing feel natural and valuable, not forced or gimmicky. When users genuinely want to show your prototype to others, you know you've created something lovable.
Step 6: Validate Through Behavior, Not Surveys
Traditional MVP validation relies heavily on user feedback and surveys. Lovable prototypes validate through observed behavior - specifically, emotional engagement metrics like time spent, voluntary returns, and unprompted sharing.
I track what I call "love signals": How long do users spend exploring? Do they return without prompting? Do they share without incentives? Do they ask when more features will be available? These behaviors are far more predictive of product success than survey responses.
Emotional Design
Focus on creating genuine emotional connections rather than just functional interactions
Personalization Engine
Use AI to make every user feel like the product was built specifically for them
Progressive Discovery
Reveal capabilities gradually to maintain simplicity while hinting at deeper value
Viral Mechanics
Build sharing into the core experience so users naturally want to show others
The results of this approach have been consistently better than traditional MVP development, both in terms of user engagement and business validation.
Looking at projects where I applied this lovable prototyping framework versus traditional MVP approaches, the differences are stark. Lovable prototypes typically see:
3-5x higher time-on-site during initial sessions
40-60% voluntary return rates within the first week (vs. 10-15% for functional MVPs)
Organic sharing rates of 15-25% without any incentive systems
2x more detailed user feedback because people are emotionally invested
But the most important result isn't quantitative - it's qualitative. Lovable prototypes create genuine excitement rather than polite interest. Users start asking about roadmaps, suggesting features, and introducing the product to their networks.
One AI prototype I built for a client validation project generated over 200 email signups in its first week, with zero marketing spend. Users were sharing it because they genuinely enjoyed the experience, not because they were incentivized to do so.
The business validation is also more reliable. When users love a prototype, they're much more likely to become paying customers when the full product launches. We've seen conversion rates from prototype users to paid customers reach 30-40%, compared to 5-10% for traditional MVP-to-product pipelines.
Perhaps most importantly, lovable prototypes save time and money by providing clearer validation signals. You know much faster whether you're building something people actually want, because the emotional engagement data is immediate and unambiguous.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After implementing this approach across multiple client projects and my own product experiments, several key patterns have emerged:
Emotion beats function in early validation - Users forgive missing features if they love the core experience, but they won't forgive boring functionality no matter how complete it is.
AI enables personalization at prototype scale - You can create individually tailored experiences without building complex user management systems.
"Demo magic" is more valuable than feature completeness - One impressive 30-second interaction is worth more than 20 mediocre features.
Sharing behavior is the best validation metric - If users don't voluntarily share your prototype, they probably won't love your finished product.
Progressive revelation keeps users engaged longer - Gradually unveiling capabilities creates a sense of discovery that maintains interest.
The constraint isn't technology anymore, it's attention - With AI tools making development easier, the real challenge is creating something people care about.
Lovable prototypes create better feedback loops - Emotionally engaged users provide more detailed, actionable insights than users who are just "trying out" your product.
The biggest lesson? In an age where anyone can build an MVP in a weekend, lovability becomes the differentiating factor. The question isn't whether you can build your idea - it's whether you can build your idea in a way that makes people genuinely excited to use it.
This doesn't mean over-designing or feature-bloating. It means being intentional about emotional experience design from day one, using AI to create personalized interactions, and validating through behavior rather than just feedback.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups looking to implement lovable AI prototyping:
Start with one AI-powered feature that creates immediate "wow" moments during onboarding
Use AI to personalize the user interface and recommendations from the first interaction
Build sharing into your core workflow - make success visible and shareable
Track emotional engagement metrics like session length and voluntary returns over feature usage
For your Ecommerce store
For ecommerce businesses applying this framework:
Use AI to create personalized product discovery experiences that feel almost magical
Focus on AI-powered features that make customers feel understood and valued
Build prototype experiences around your most impressive AI capabilities first
Create shareable moments in the shopping experience that customers want to show friends