Developers are very good at building.
The problem is that we often build before we know whether the idea is worth building.
I built Market Verdict because I needed it myself.
I could not get the kind of job I wanted, so I started looking seriously at a career pivot. But even as a veteran coder, I had a hard time figuring out what was realistic.
Should I build a SaaS?
Should I create a product?
Should I start a service business?
Should I focus on AI, local businesses, SEO, automation, or something else?
I did not need generic motivation.
I needed something that could look at an idea and help me judge whether it had a realistic path forward.
That is how Market Verdict was born.
I wanted a tool that could take a business idea and return a structured, practical analysis before I spent months building the wrong thing.
What Market Verdict does
Market Verdict analyzes a business idea and returns:
- A viability score
- Market signals
- Competitor signals
- Risk factors
- Break-even estimate
- Suggested pivots
- Execution plan
- Saved idea history
- Exportable reports
The goal is not to magically predict the future.
The goal is to reduce bad guesses.
Most business ideas fail because the founder starts with enthusiasm but not enough evidence. Market Verdict is meant to slow that process down just enough to ask better questions before committing serious time and money.
Why I built it in Go
The backend is written in Go.
I chose Go because I wanted:
- Fast server-side rendering
- Simple deployment
- Strong standard library
- Easy concurrency
- Clean HTTP handlers
- Predictable performance
- Low operational complexity
Another major reason was goroutines.
A lot of the system depends on background work. Goroutines are a natural fit for that.
I use them for background processes, automation, long-tail SEO workflows, and other jobs that need to run without blocking the main user experience.
For example, I have built systems around:
- Auto-generated blog content
- Long-tail SEO pages
- Background processing
- Internal automation
- Scheduled content workflows
- Search-oriented landing pages
At this point, I have generated nearly 50,000 URLs with unique content as part of the broader Market Verdict SEO system.
That kind of background workload is one of the reasons Go fits the project well.
The app uses a traditional web architecture instead of a heavy frontend framework. The frontend is mostly server-rendered HTML with HTMX and vanilla JavaScript where needed.
That keeps the product fast, simple, and easier to maintain.
The stack
The current stack includes:
- Go backend
- PostgreSQL
- HTMX
- Vanilla JavaScript
- HTML templates
- Stripe for payments
- Magic-link authentication
- Server-side sessions
- PDF and TXT exports
- Docker-based local development
- Render deployment
I wanted the app to feel lightweight but still behave like a real SaaS product.
That meant adding the less glamorous parts too:
- Auth
- Payment flow
- Saved ideas
- User limits
- Free analysis tracking
- Export routes
- Tests
- Error pages
- Privacy and terms pages
- Sitemap and robots files
- Basic abuse prevention
Testing the product seriously
One thing I did not want was a fragile demo.
Market Verdict has more than 90% unit test coverage and close to 300 Playwright tests.
The Playwright tests cover the actual user flows, including:
- Login
- Magic-link auth
- Idea submission
- Analysis generation
- Saved ideas
- Payment-related routes
- Export routes
- Error states
- Public pages
- Auth-gated pages
For a SaaS product, especially one with payments and authentication, the boring parts matter.
It is not enough for the main feature to work once on your machine.
The full user flow has to keep working after changes.
The hard part: scoring without pretending to be magic
One of the hardest parts was deciding how to score an idea.
A business idea is not like a math problem where there is one correct answer.
So the score has to be useful without pretending to be absolute truth.
The approach I took was to combine multiple signals:
- How clear the idea is
- Who the customer is
- Whether the problem is painful
- Whether competitors already exist
- Whether there is obvious demand
- Whether the business can plausibly make money
- Whether the execution path is realistic
The score is useful only when paired with the explanation.
A number by itself is not enough.
A good analysis should tell the user:
“This might work, but here are the weak spots.”
or:
“This idea is too broad. Narrow the audience and test a smaller version first.”
That is the real value.
Why I avoided making it just another chatbot
A user could technically ask an AI:
“Is my business idea good?”
But the answer will usually be too broad unless the user knows exactly what to ask.
Market Verdict is designed to produce a structured answer every time.
Instead of an open-ended chat, the app gives users a repeatable report format:
- Summary
- Score
- Market analysis
- Competitor context
- Revenue assumptions
- Risks
- Suggested next steps
That makes it more useful for comparing multiple ideas.
It also helps non-technical founders who do not want to engineer prompts.
What I learned building it
A few lessons stood out.
First, people do not just want “AI output.”
They want decisions.
A wall of text is less useful than a clear recommendation, a score, and next steps.
Second, business validation needs to be specific.
“Start a coffee shop” is not enough.
“Start a coffee shop near a university with late-night study hours and subscription coffee plans” is much more useful to analyze.
Third, simple UX matters.
The app needs to feel like:
- Enter idea
- Get answer
- Save or export
- Improve the idea
- Try again
Anything more complicated creates friction.
Fourth, distribution matters as much as code.
Building the product is only one part of the work.
The rest is SEO, content, testing, onboarding, pricing, positioning, and trust.
That is why I have also invested in long-tail SEO, automated content workflows, and thousands of targeted pages designed around specific business questions.
Current monetization model
Market Verdict currently has:
- 3 free analyses per email
- A single paid analysis option
- A monthly subscription option
I added the free tier because users need to see value before paying.
I added single purchase because not everyone wants a subscription.
I added subscription because some users will want to test many ideas over time.
What I would improve next
The next areas I am thinking about are:
- Better competitor discovery
- More local market signals
- More industry-specific scoring
- Better reports for investors
- Business idea comparison
- More language support
- Deeper execution plans
- More realistic revenue modeling
The biggest challenge is making the analysis more factual without making the product slow or expensive to run.
Try it
You can try it here:
I would be interested in feedback from other developers, founders, and people building SaaS products.
The question I am trying to answer is simple:
Can a structured validation tool help people avoid wasting months on weak business ideas?
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