Part 6 - The Decision Framework for Walking Away

I spent two weeks building something that works. Then I did the math and walked away. Here's the decision framework I used—and how to know when a working project isn't worth launching.

What Actually Worked (And Why It Still Wasn't Enough)

The investment scraper wasn't a technical failure. The technical pieces worked. The multi-tier fallback system recovered 40% of failed scrapes. Caching cut API costs 60%. Confidence scoring revealed exactly where data was incomplete. But none of this mattered when I ran the launch decision framework.

But here's the problem: none of this mattered when I ran the launch decision framework.

What Broke Constantly (The 80% Problem)

Building a scraper is 20% writing code and 80% handling edge cases. Every website is different. Every HTML structure is a special snowflake. Every extraction method fails in creative new ways.

Timeouts killed large portfolios. Edge functions max out at 60 seconds. Any private equity firm with 30+ portfolio companies? Timeout. Partial results. No retry mechanism. I needed asynchronous job queues from day one but tried to be clever with synchronous polling instead.

AI extraction fails 10-15% of the time. JavaScript-heavy sites, weird table layouts, content that loads after 3 seconds—LLMs just can't handle every edge case. My fallback system caught most failures, but some websites are genuinely unscrape-able without writing custom code per site.

No URL validation meant garbage-in-garbage-out. I assumed users would paste the right portfolio page URL. They wouldn't always. The scraper would choke on homepages, About Us pages, random blog posts. Needed input validation with working examples—didn't build it.

No retry for failed pages meant lost data. If a page timed out, that data was gone. Should've built a "Retry Failed" button that reprocessed low-confidence results using cached HTML instead of fresh API calls. Didn't prioritize it.

These weren't showstoppers individually. But together they meant constant maintenance for marginal returns. That's when I pulled out the decision framework.

The "Should I Launch This?" Decision Framework

RUN THE NUMBERS:
☐ Unit economics work? (revenue > COGS × 2.5)
☐ Scalable without linear cost growth?
☐ Maintenance burden sustainable long-term?
☐ Better ROI than other opportunities?

ASSESS THE MARKET:
☐ People actually have this problem?
☐ Willing to pay enough to make it profitable?
☐ Existing solutions inadequate?
☐ Problem worth solving at scale?

CHECK YOUR CONSTRAINTS:
☐ Time to maintain long-term?
☐ Excited to work on this for 2+ years?
☐ Can handle support/customer issues?
☐ Is this the best use of your time?

DECISION TREE:
- All boxes checked → LAUNCH
- Economics work but not excited → SHELF IT or SELL IT
- Excited but economics don't work → PIVOT or FIND DIFFERENT MODEL
- Neither → WALK AWAY (smart capital allocation)

SUNK COST DISCIPLINE:
Time already spent is IRRELEVANT to the decision.
Only future costs and returns matter.

Here's how my scraper scored:

Unit economics: Failed. Firecrawl costs scale linearly with page volume. Every new customer meant proportional API costs with no economies of scale. Even with caching, margins were too thin. To hit 2.5× cost coverage, I'd need to charge $100+/month for a tool most users would run 2-3 times maximum.

Scalability: Failed. More users meant more support tickets for edge cases. Each failed scrape required manual investigation. JavaScript-heavy sites needed custom rules. That's linear growth in maintenance burden—the opposite of scalable SaaS.

Market assessment: Uncertain. Private equity analysts might pay for this, but the addressable market felt small. Existing solutions (manual copy-paste, hiring VAs) were inadequate but cheap. Would users pay premium pricing for automation? Probably not enough of them.

Personal constraints: Failed. I wasn't excited about maintaining a scraper for 2+ years. Customer support for "Why didn't your tool scrape this weird site correctly?" sounded exhausting. This was a learning project that accidentally became a product candidate—not something I wanted to run as a business.

Verdict: Walk away. Not because it didn't work—because the opportunity cost was too high.

What I'd Do Differently (If Economics Changed)

If someone asked me to rescue this project, here's what I'd demand first: proof that 100+ people will pay $150/month. Without that validation, here's what I'd build:

User authentication first. Can't charge users or save scraping history without accounts. Firebase Auth would take half a day to implement. Gate features behind tiers: free users get 10 scrapes/month, paid users get unlimited plus batch mode.

Batch URL scraping as the killer feature. Let users upload a CSV with 20 PE firm URLs and scrape them all overnight. Send an email when done. This is what would justify premium pricing—scraping one site at a time is barely better than doing it manually.

Advanced filtering for large datasets. Once you scrape 200 portfolio companies, you need to filter by industry, investment year, location, status. Raw data dumps are useless without sortable columns, search, and filtered exports.

AI-powered data enrichment. Use Claude or GPT to standardize industry categories, estimate company size from descriptions, flag potential exits. The AI's already in the stack—might as well leverage it beyond extraction.

Analytics dashboard for visual impact. Show aggregate stats: "You've scraped 47 firms, 342 companies, across 18 industries." Charts for industry distribution, investment timing trends, geographic concentration. Makes the tool feel professional instead of just a data extractor.

But here's the thing: these features wouldn't fix the unit economics. They'd make the product more polished, but Firecrawl costs would still scale linearly. That's a structural problem no amount of features can solve.

Key Takeaway: When Working Projects Aren't Worth Launching

The hardest business decision isn't killing broken projects—it's walking away from working ones that don't pencil out.

Most founders struggle with sunk cost fallacy. "I already spent three months building this, I have to launch it." No you don't. Time already spent is irrelevant. Only future costs and returns matter.

The scraper taught me more than any tutorial could: how to handle AI failures, when caching matters, why modular architecture saves your sanity, what makes scraping hard at scale. Those lessons transferred to other projects with better economics.

Sometimes the best education is building something real, watching it not pencil out, and having the discipline to move on. That's not failure—it's smart capital allocation.

The mark of good judgment isn't always shipping. Sometimes it's knowing when to stop.

Your turn: Pull out that side project you've been hesitating on. Run it through the decision framework above. Does it pass? Does it fail? Reply with your score—I'll tell you if I see something you missed.