The Startup Lottery Problem
You know the feeling. You spend months—sometimes years—building something you think people need. You solve your own annoyances, you follow trends, you listen to advice. And then... crickets. The market doesn't care. You've just played another round of the startup lottery, and you lost.
I've been there. Built three different SaaS tools between 2023 and 2025, each solving what I thought were real problems. Each one failed to gain traction. The common thread? I was solving annoyances, not liabilities. There's a massive difference between something that bothers someone and something that could cost them their business.
That realization led me to a different approach. Instead of guessing what businesses might want, I decided to look at where they're legally forced to spend money. If a company is paying fines, settling lawsuits, or dealing with regulatory penalties, they're not just annoyed—they're bleeding cash. And they'll pay to stop the bleeding.
Why Court Filings Are a Goldmine
Here's the insight that changed everything: when businesses get sued or fined, that information becomes public record. We're talking about actual dollars leaving their accounts—not hypothetical pain points, not survey responses about what might be valuable. Real money. Real problems.
Last month, I burned through about $5,000 in API credits to automate what I call "liability research." I scraped 48,000 US District Court filings and regulatory fine databases. The goal wasn't to find the biggest lawsuits (though those are interesting). It was to find patterns—recurring issues that affect lots of businesses in the same way.
Think about it this way: if one restaurant gets sued for ADA compliance issues, that's a problem for that restaurant. If fifty restaurants in the same state get sued for the same ADA issues within six months, that's a market. And that market is already spending money—on lawyers, on settlements, on fines. They're primed to spend on solutions.
The Methodology: From Data to Dollars
Let me walk you through how this actually works, because several people in the original discussion asked about the technical details. First, you need access to court data. PACER (Public Access to Court Electronic Records) is the main source for federal cases, but there are also state court databases and regulatory agency sites.
The challenge? This data is messy. Really messy. Different courts use different formats, case names follow different conventions, and the actual complaint documents are often PDFs that need parsing. That's where automation comes in.
I used a combination of tools to handle this. For the initial scraping and data extraction, Apify's web scraping platform handled the heavy lifting of rotating proxies and managing headless browsers. For document parsing, I built custom scripts using Python libraries like PyPDF2 and spaCy to extract meaningful information from those thousands of PDFs.
The key was looking for specific patterns: class action lawsuits (affecting many businesses), regulatory fines with clear violation codes, and settlements with disclosed amounts. I filtered for cases where the alleged damages or fines were between $10,000 and $500,000—big enough to matter, but not so big that only Fortune 500 companies would be involved.
Niche #1: ADA Website Compliance (The $150k Blind Spot)
This was the most consistent pattern across all the data. In 2025 alone, I found over 1,200 lawsuits filed against small to medium businesses for website accessibility violations under the Americans with Disabilities Act. We're not talking about tech companies here—I'm talking about local restaurants, retail stores, service providers.
Here's what happens: a plaintiff (often represented by one of a handful of law firms that specialize in this) sues because the business's website isn't accessible to screen readers, doesn't have proper alt text for images, or has navigation that can't be used with keyboard-only controls. The businesses typically settle for $5,000 to $20,000 plus attorney fees, plus they have to fix the website.
But here's the opportunity—most of these businesses don't know how to stay compliant. They fix it once, then their marketing team adds a new page six months later without proper alt tags, and they're vulnerable again.
A service that provides ongoing ADA website compliance monitoring and fixes could charge $200-$500 per month. For a business that just paid $15,000 to settle a lawsuit, that's a no-brainer. And you don't need to be a developer—you can use existing tools like accessiBe or UserWay and white-label them, or hire a developer on Fiverr to build a simple monitoring dashboard.
Niche #2: Restaurant Wage & Hour Documentation
This one surprised me with how consistent it was. Across multiple states, restaurants with 10-50 employees were getting hit with wage and hour lawsuits. The specific issue? Improper tracking of breaks and overtime for hourly employees.
The lawsuits typically allege that employees weren't getting proper meal breaks, or that overtime wasn't calculated correctly when employees worked split shifts. These cases often become class actions, meaning all current and former employees in a certain period can join. Settlements ranged from $40,000 to $150,000.
The root problem is usually paper time cards or basic digital systems that don't properly track the specific requirements of restaurant labor laws. Managers manually adjusting hours, employees forgetting to clock out for breaks, that sort of thing.
The solution isn't another generic time tracking app—it's a system built specifically for restaurant compliance. Think automated break reminders, overtime alerts before it happens, and audit-ready reporting. You could start this as a service using modified versions of existing time tracking software, then build your own specialized solution once you have traction.
\nNiche #3: Contractor Licensing & Bond Verification
This came up repeatedly in construction industry lawsuits. General contractors were getting sued when subcontractors they hired turned out to be unlicensed or underinsured. When something went wrong on the job site, the GC was left holding the bag—sometimes for six figures in damages.
The data showed a clear pattern: contractors were hiring subs based on word-of-mouth or lowest bid, without systematically verifying their current licenses, insurance certificates, and bond status. When a sub's license had lapsed or their insurance had expired, the GC became liable.
This creates an opportunity for a verification service that automatically checks contractor licenses and insurance status on a recurring basis. Not just a one-time check—ongoing monitoring with alerts when something is about to expire. You could charge $50-$100 per contractor per month for this peace of mind.
What makes this particularly viable in 2026 is that many states now have APIs for their licensing databases. You're not manually checking websites—you're building automated systems that do the verification work. Start by focusing on one state with good API access, then expand.
How to Validate Your Own Court Data Findings
Okay, so you're excited about this approach. Maybe you want to look for other patterns in court data. Here's my step-by-step process for validation—because finding a pattern is just the first step.
First, quantify the financial pain. Look for actual settlement amounts or fines in the court documents. If businesses are paying $10k+ to make a problem go away, that's real pain. If it's mostly small claims court stuff under $5k, the pain might not be severe enough.
Second, check for recurrence. Is this happening to multiple businesses in the same industry? Look for at least 20-30 similar cases in the last year. One-off lawsuits might be unique situations, but patterns indicate systemic problems.
Third, talk to the businesses. This is where most people in the original discussion said they'd struggle. But it's simpler than you think. Find businesses that have been sued (their names are in the court records), wait 3-6 months after the case settles (so they're not in active litigation), and reach out. Say something like: "I noticed your business recently dealt with [specific issue]. I'm researching solutions in this area—would you have 10 minutes to share what that experience was like?"
Most will say no. Some will talk. Those conversations are pure gold for understanding the real problem, not just what's in the legal documents.
Common Mistakes (And How to Avoid Them)
Based on the questions and concerns in the original discussion, here are the pitfalls I see people falling into with this approach:
Mistake #1: Chasing the biggest dollar amounts. Yes, that $10 million lawsuit against a pharmaceutical company is interesting. But can you actually sell to pharmaceutical companies? Probably not. Focus on businesses you can actually reach and sell to—small to medium businesses in industries you understand.
Mistake #2: Building too complex of a solution. You don't need to solve 100% of the problem on day one. For the ADA compliance niche, you could start with a manual audit service using Screen Reader Software and a checklist. Get paying customers first, then automate.
Mistake #3: Ignoring the sales cycle. Businesses dealing with lawsuits are in crisis mode. They want solutions now. But once the lawsuit settles, urgency disappears. Your marketing needs to catch them at the right moment, or create urgency around prevention.
Mistake #4: Underestimating legal complexity. You're not a lawyer. Don't give legal advice. Your solution should be a tool or service that helps with compliance, not a substitute for legal counsel. Always include disclaimers and consider partnering with a law firm for the actual legal aspects.
The Tools You Actually Need (2026 Edition)
Let's get practical. If you want to try this approach yourself, here's what you actually need:
For data collection: Start with Apify's ready-made scrapers for court websites. It handles proxy rotation and CAPTCHAs, which are the biggest headaches. The learning curve is much lower than building your own scraping infrastructure.
For data analysis: Honestly, you can start with Excel or Google Sheets. Look for patterns manually at first. Once you have a hypothesis, then you can use Python with pandas for deeper analysis. Don't over-engineer this phase.
For building the solution: Start with no-code or low-code tools. Bubble, Softr, or even a well-configured WordPress site with plugins. Prove people will pay before you write a single line of custom code. I wasted months building before validating—don't make my mistake.
For learning the domain: Read the actual court complaints. They lay out exactly what went wrong. Also, Business Compliance Books can give you the foundational knowledge you need to understand the regulations involved.
FAQs from the Original Discussion
"Isn't this ambulance chasing?" No, because you're not targeting businesses currently in lawsuits. You're identifying patterns of problems that affect many businesses, then building preventative solutions. There's a big ethical difference.
"What about data privacy concerns?" Court filings are public records. You're not accessing private information. However, be respectful in how you use this data—don't spam businesses that have been sued.
"Can I do this outside the US?" Absolutely. Many countries have public court records. The principles are the same, though the specific regulations will differ.
"How much technical skill do I need?" Less than you think. Basic understanding of data analysis helps, but you can hire someone on Fiverr for the technical parts. Your real value is in pattern recognition and solution design.
Your Next Steps
Here's what I want you to do today. Don't just read this and move on. Pick one of the niches I mentioned—ADA compliance, restaurant wage tracking, or contractor verification. Spend one hour researching it further. Look up recent lawsuits in your area. Check what solutions already exist.
Then, talk to one business owner in that industry. Not to sell anything—just to understand their world. Ask about their biggest compliance headaches. You'll be amazed at what you learn.
The startup lottery is optional. You don't have to guess what businesses might want. The data is literally there in public records, showing you exactly where they're losing money. Your job is to build the stopgap.
In 2026, the most profitable opportunities aren't in the shiny new tech trends. They're in the boring, overlooked problems that businesses are already paying to solve—they're just paying lawyers instead of you. Go find them.
