How I took an app idea from concept to launch in 24 hours using ai development
- Chris Marshall
- 2 days ago
- 5 min read
What I Was Able To Achieve
In less than 24 hours, I went from a messy spreadsheet to a fully functioning SaaS product complete with user authentication, loan tracking, amortization schedules, and Stripe-powered subscriptions live on the web at LoanLog.io.
This wasn’t done with a team of developers, months of sprint planning, or a six-figure budget. It was achieved by combining Firebase Studios’ AI-powered app builder with ChatGPT 5’s debugging and problem-solving capabilities.
The result: a launchable MVP that solves a real-world problem for small-scale lenders and proof that with today’s AI tools, anyone can build and deploy software faster than ever before.
The Client Profile (Me as “the client”)
Industry: Personal finance / peer-to-peer lending
Challenge: Tracking multiple small loans efficiently without enterprise-grade software
Context: Casual lenders like me (individuals, not institutions) often lend money but lack tools to manage terms, payments, and reminders.
This case study tells the story of how that challenge became the opportunity for LoanLog.
The Challenge
The starting point wasn’t glamorous. I had been lending small amounts of money to peers for years and tracking everything in spreadsheets. While spreadsheets worked for a single loan, they quickly became unmanageable:
No automated reminders for borrowers.
No clean amortization schedules when payments varied.
Borrower contact info stored across emails, files, and notes.
Loan documents scattered in folders.
Growing risk of errors as more loans were added.
The alternatives didn’t fit:
Borrower-focused apps only helped people manage debt, not lenders manage loans.
Enterprise loan platforms were built for banks and private lenders managing millions, not for individuals.
DIY hacks with spreadsheets and formulas were fragile and time-consuming.
In short: there was a gap in the market for a lightweight, SaaS-style loan tracker designed for small-scale lenders.
But the bigger challenge? I didn’t want to spend months or tens of thousands of dollars building it.
The Turning Point
I knew the problem intimately, I had the concept for a solution, but I was on the verge of not building it at all.
Why? Because traditional software development is slow and expensive. Hiring developers, managing timelines, iterating over months wasn't worth it for something I wasn’t even sure people would pay for.
So the danger was that this idea, like many others, could have sat in a notebook and died there.
What changed was my growing experience with AI-powered development tools, particularly Firebase Studios. I’d already tinkered with small projects there, and one Wednesday night, I decided: Let’s see how far I can get in a day.
The Solution
Within 24 hours, using a combination of Firebase Studios and ChatGPT 5, I launched LoanLog, a SaaS MVP for small lenders.
Here’s what made it possible:
Firebase Studios Prototyper:
Instantly set up backend infrastructure (databases, authentication, hosting).
Generated a working frontend with forms and dashboards.
Enabled fast iteration via conversational prompts.
A FREE tool with unlimited usage unlike many of the other Ai development software
ChatGPT 5 Debugging:
Diagnosed and explained tricky errors that Firebase AI got stuck on.
Provided clean, working code suggestions.
Allowed me to “feed fixes” back into Firebase’s AI loop until issues were resolved.
Stripe Integration:
Added subscription payments so the MVP wasn’t just functional, it was monetizable.
The Process
Phase 1: Laying the Foundation
That Wednesday night, I sat down and described the app I wanted to Firebase Studios. In minutes, the Prototyper created:
Authentication for user accounts.
A Firestore database with loan and borrower models.
Hosting scaffolding.
I went to bed with a working skeleton.
Phase 2: Building Features With Ai Development
The next morning, I expanded the app:
Loan creation with terms and balances.
Borrower contact entry.
Amortization schedule generation.
Dashboard to view loans.
The foundation was usable, but errors appeared. Firebase AI sometimes looped on fixes, trying the same broken solution repeatedly.
Phase 3: The Debugging Breakthrough
I almost stalled out. The errors weren’t fatal, but they could have slowed progress for days if I personally went through and tried to figure out the error and fix the code myself.
Instead, I copied the relevant code into ChatGPT, explained the bug, and let it analyze. Within seconds, it found the exact problem, explained what needed to be done, and offered a clean solution including examples of how to structure or write the code.
I fed those fixes back into Firebase, and the app kept moving forward. This back-and-forth, Firebase to build and ChatGPT to debug, became the heartbeat of the project.
Phase 4: Monetization and Launch
By the afternoon, I had a functional app. By evening, I had integrated Stripe payments, deployed the app with Firebase Hosting, and launched it live.
LoanLog went from concept to launchable MVP in less than a day.
The Results
Here’s what LoanLog delivered in 24 hours:
✅ User authentication & onboarding
✅ Loan creation with custom terms
✅ Automated amortization schedules
✅ Borrower contact info storage
✅ Subscription payments with Stripe
✅ Live deployment at app.loanlog.io
Time saved: Months of development, condensed into a single day.
Cost saved: Tens of thousands in developer hours.
Outcome: A monetizable MVP solving a real problem, ready for users.
Data & Specific Numbers
Traditional build estimate: 3–6 months, ~$50k–$100k in development.
Actual build with AI: ~24 hours, near-zero development cost just hosting (Blaze pay-as-you go plan), Stripe (Takes a % of subscriptions or payments) and the domain name ($16 through Namecheap for the year).
Features shipped: 6 major features in one day.
Error resolution rate: ChatGPT solved 100% of errors Firebase got stuck on within 1–2 iterations.
Lessons Learned
AI tools aren’t perfect, but they’re complementary. Firebase Studios is fast at generating, ChatGPT is brilliant at debugging. Together, they’re powerful.
Launch > polish. LoanLog isn’t done, but it’s live, monetizable, and ready for feedback.
Always use the Prototyper. It handles the boring backend setup instantly.
Numbers matter. 24 hours to get something off the ground.
This system is repeatable. I’ve since built three MVPs in weeks using the same workflow.
The Strategy Going Forward
As LoanLog proves a value to users, Utilizing the same type of workflow it will be easy to incorporate the following.
Short term: Add borrower reminders, document uploads, and reporting.
Medium term: Launch a mobile app version.
Long term: Test adoption among casual lenders and scale.
Conclusion
LoanLog is more than just a personal success story, it’s proof of a broader reality: with AI, the barriers to building software have collapsed.
What once required a team, months, and capital can now be done by an individual in a single day. The opportunity for entrepreneurs, indie hackers, and SMB owners is enormous.
Your idea doesn’t need six months. It needs 24 hours.
Want More?
I’m planning a live session where I’ll build a brand-new SaaS app from scratch in one sitting, using the same process. Want to watch? Register here.
Want help developing some software or building out an AI or automated system for your company or organization? Check out my Ai and Automation Firm Xemplar Labs.
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