Originally published at chudi.dev
I had a backlog of 47 SaaS ideas. Most would never get built.
The bottleneck wasn't creativity—it was execution. Each idea requires:
- Market research
- Problem validation
- Architecture planning
- Actual coding
- Deployment
- Billing integration
Weeks of work before you know if anyone will pay.
So I built a system to do it for me.
Introducing MicroSaaSBot
MicroSaaSBot is an AI system that takes a problem statement and outputs a deployed SaaS product.
Input: "Bookkeepers spend 10+ hours weekly transcribing bank statements to spreadsheets."
Output: StatementSync—a live product with user auth, PDF processing, and Stripe billing.
Time: One week.
This isn't hypothetical. StatementSync is live. Users are paying. The AI built it.
The Four Agents
MicroSaaSBot uses specialized agents for each development phase:
Each agent is optimized for its phase. The Researcher agent knows nothing about coding. The Developer agent doesn't care about market research. Specialization enables excellence.
The Workflow
Phase 1: Validation
You provide a problem statement:
"Bookkeepers spend 10+ hours weekly transcribing bank statements to spreadsheets."
The Researcher agent investigates:
- Who has this problem? (Persona definition)
- How severe is it? (Pain scoring)
- Are they paying for solutions? (Willingness to pay)
- What solutions exist? (Competitive landscape)
Output: Problem score (0-100).
Problems scoring below 60 get killed. No architecture, no coding, no wasted effort. This is the most important feature—stopping bad ideas early.
StatementSync scored 78/100:
- Severity: 8/10 (daily pain)
- Persona clarity: 9/10 (freelance bookkeepers)
- Willingness to pay: 8/10 (already paying competitors)
Green light. The complete day-by-day timeline of how this validation played out is in the full idea-to-MVP walkthrough.
Phase 2: Architecture
The Architect agent designs the system:
Frontend: Next.js 15 (App Router)
Auth: Clerk
Database: Supabase PostgreSQL
Storage: Supabase Storage
Payments: Stripe
PDF Processing: unpdf
Hosting: Vercel
Key decisions are surfaced for human approval:
- "Using pattern-based extraction (faster, cheaper) vs LLM extraction (more flexible). Recommend pattern-based for cost control. Approve?"
- "Flat-rate pricing vs per-file. Recommend flat-rate for user acquisition. Approve?"
You make the strategic calls. The agent handles implementation details. The reasoning behind the flat-rate pricing recommendation is in flat-rate vs per-file SaaS pricing.
Phase 3: Development
The Developer agent builds features:
- User authentication flow
- File upload handling
- PDF parsing engine
- Export generation (Excel, CSV)
- Billing integration
- Dashboard UI
Each feature includes:
- Implementation code
- Error handling
- TypeScript types
- Basic tests
Development happens in phases—each phase builds on the previous, with checkpoints for review.
Phase 4: Deployment
The Deployer agent ships:
- Vercel project configuration
- Supabase database setup
- Stripe product/price creation
- Webhook configuration
- Environment variables
- DNS and domain setup
Output: A live URL with working product.
What Humans Still Do
MicroSaaSBot handles the tedious 80%. Humans handle the meaningful 20%:
Strategic decisions:
- Approve/reject validation scores
- Choose between architectural options
- Set pricing and positioning
- Define brand/design preferences
Business operations:
- Marketing and sales
- Customer support
- Financial management
- Legal/compliance
Quality judgment:
- Review generated code
- Test edge cases
- Approve deployment
- Monitor production
Think of MicroSaaSBot as a senior engineer who executes your vision. You're still the founder. You make the decisions that matter.
What Makes a Good MicroSaaS Idea
Not all problems survive the validation phase. After running dozens of ideas through MicroSaaSBot's Researcher agent, the pattern of what fails is clear.
High-scoring problems (70+):
- Specific, named persona with a clearly observed behavior ("freelance bookkeepers who process 50+ PDFs monthly")
- Existing paid solutions with obvious gaps (competitors exist but users complain about cost or friction)
- Daily or weekly recurrence (not an occasional inconvenience)
- Quantifiable time cost ("10+ hours per week")
Low-scoring problems (below 60):
- Vague personas ("small businesses" or "busy professionals")
- Problems with free alternatives that are "good enough"
- Pain points that disappear when the user upgrades their workflow
- Markets that require enterprise sales or custom contracts
The scoring rubric isn't arbitrary—it reflects where most SaaS products die. Vague personas lead to positioning that resonates with nobody. Problems with free alternatives lead to CAC that never recovers. MicroSaaSBot's kill threshold at 60 exists because the system has seen enough failed validations to know which signals predict viable products.
The counterintuitive finding: niche is better. A product for "freelance bookkeepers who process bank statements" outperforms a product for "anyone who works with documents." Specificity creates referrals, and referrals have zero CAC.
The First Success
StatementSync is proof this works:
| Phase | Duration | Output |
|---|---|---|
| Validation | 2 days | 78/100 score, approved |
| Architecture | 1 day | Tech stack, schema, approved |
| Development | 3 days | All features implemented |
| Deployment | 1 day | Live on Vercel with Stripe |
| Total | 7 days | Production SaaS |
The product converts PDFs to spreadsheets. Users pay $19/month. It works.
Why This Matters
The traditional path:
- Have idea (Day 1)
- Research market (Week 1-2)
- Plan architecture (Week 2-3)
- Build MVP (Week 4-8)
- Deploy and iterate (Week 9+)
- Maybe get users (Month 3+)
The MicroSaaSBot path:
- Have idea (Day 1)
- Validated + deployed (Day 7)
- Get users (Week 2)
Speed matters because:
- You learn faster
- You fail cheaper
- You iterate sooner
- You validate with real users, not assumptions
The Bigger Picture
MicroSaaSBot isn't just a productivity tool. It's a different way of building.
Traditional: Humans do everything, AI assists with code completion.
AI-first: AI handles the workflow, humans make strategic decisions.
The shift is from "AI helps me code" to "AI builds the product, I run the business."
This is where product development is heading. MicroSaaSBot is my bet on that future.
The Hard Parts AI Doesn't Solve
MicroSaaSBot compresses the execution timeline significantly. But it doesn't eliminate the hard problems in building a SaaS business.
Product-market fit is still discovered through user behavior, not agent validation. An 78/100 validation score means the problem is real and the persona is specific—it doesn't guarantee that your specific implementation solves it the way users want. StatementSync's first design put export buttons in the wrong place; users had to tell me that.
Pricing psychology requires market intuition. MicroSaaSBot can compare pricing models mathematically (flat-rate vs. per-file break-even), but deciding whether $19 or $29 anchors better for bookkeepers required thinking through their budget context, not running more analysis.
Distribution doesn't exist until you build it. MicroSaaSBot ships a product to a URL. Getting the first 10 paying customers still requires showing up in communities, writing about the problem, and doing things that don't scale. The product being built faster doesn't change how long distribution takes.
The right frame isn't "AI replaces founders." It's "AI eliminates the execution bottleneck so founders can focus on distribution, customers, and judgment." The work that matters most is still yours.
The Iteration Cycle After Launch
MicroSaaSBot handles the build. The period after launch requires a different workflow—one that's mostly human.
The first 30 days after shipping StatementSync were user research: watching what users did, where they got confused, which features they ignored. AI agents aren't good at this yet. Interpreting a heatmap or reading a support conversation requires judgment about what the user was actually trying to do versus what they said they were trying to do.
What worked was a simple post-launch review cycle:
- Week 1: Watch every user session (session replay tools like Hotjar)
- Week 2: Interview any user who sent a support message
- Week 3: Identify the one feature change with the highest friction impact
- Week 4: Build and ship that change
The Developer agent handled Week 4. Weeks 1-3 were entirely human.
This loop doesn't need MicroSaaSBot. It needs you paying attention. The system gave you a product in 7 days so you could start this loop faster—not so you could skip it.
The fastest path to product-market fit isn't faster building. It's faster learning. MicroSaaSBot compresses the build so you spend more time learning.
What's Next
The roadmap:
- More product types - Expand beyond web SaaS to APIs, browser extensions, automation tools
- Iteration system - Handle post-launch features and improvements
- Analytics integration - Let the Researcher agent learn from production data
- Template library - Pre-validated patterns for common product types
StatementSync was the first. It won't be the last.
Related: MicroSaaSBot Multi-Agent Architecture | Portfolio: MicroSaaSBot
Top comments (0)