FIG 2.0 — Teardown Gallery
47 systems. 12 tech stacks. Every failure we've diagnosed.
Real codebase diagnosis examples from real companies. Each teardown: the pattern, the root cause, the outcome. Your developer left no documentation? We've seen it 284 times.
E-commerce platform losing $12K/month in failed checkouts
Solo developer left after 3 years. No documentation. Payment gateway throwing silent errors on 18% of transactions. Team couldn't trace the failure because the error handling was in a custom middleware nobody understood.
Root cause: Custom retry middleware was silently swallowing Stripe webhook failures. The original developer had built a queue system that depended on a Redis instance running on his personal server — which went offline when he left.
Fix: Migrated webhook handling to Stripe's native retry mechanism, removed the custom middleware entirely, added proper error logging. Revenue loss stopped within 24 hours of deployment.
SaaS startup paying $4,200/month in cloud bills for 200 daily users
Previous agency sold them a "scalable Kubernetes architecture" for an app serving 200 requests/day. 14 microservices. 3 managed databases. A service mesh. Monthly bill: $4,200. Actual compute needed: a single $40 VPS.
Root cause: Architecture astronautics. The previous agency billed by complexity, not by need. They deployed a microservices architecture appropriate for 100K+ users onto an app that hadn't validated product-market fit yet.
Fix: Consolidated 14 services into a single Node.js monolith. Moved from managed K8s to a single Railway deployment. Cloud bill dropped from $4,200/month to $67/month. Performance actually improved because inter-service network latency disappeared.
Marketplace app — page loads hit 8 seconds after reaching 5,000 listings
Laravel app worked perfectly at 500 listings. At 5,000, the search page took 8 seconds. At 10,000 the admin panel became unusable. The team's proposed fix: "rewrite in microservices." Actual fix: 4 database queries.
Root cause: Classic N+1 query problem compounded by eager loading relationships 4 levels deep. The search page was making 5,000+ individual database queries per page load. No indexing on the columns used in WHERE clauses.
Fix: Added composite indexes on 3 tables, replaced eager loading with selective lazy loading, implemented query caching on the search endpoint. Total code changes: 47 lines. No architectural rewrite needed. Load time dropped to 400ms.
AI customer service bot giving incorrect refund information 31% of the time
Company deployed a RAG-based chatbot for customer support. Within 2 weeks, customers were getting confidently wrong answers about refund policies, shipping times, and product specifications. Trust score dropped from 4.2 to 2.8 stars.
Root cause: Three problems. (1) RAG retrieval matched on semantic similarity but ignored recency — pulling outdated policy docs. (2) No validation layer between model output and user. (3) The prompt allowed the model to "infer" answers when it couldn't find exact matches.
Fix: Added deterministic validation gate: if retrieved docs don't contain an exact policy match, bot escalates to human. Implemented date-aware retrieval. Added confidence scoring with auto-escalation below threshold. Hallucination rate: 31% → 3%.
Agency client site — 11-second load time, every update breaks something
52 plugins. 2 page builders (Elementor AND WPBakery). A theme from 2018. Custom PHP snippets injected via 3 different "code snippet" plugins that conflicted with each other. Client paying $800/month to an agency that just kept adding plugins.
Root cause: Plugin collision. 52 plugins, 14 doing overlapping things (3 caching plugins fighting each other, 2 SEO plugins, 4 security plugins). Page builders generated 340KB of inline CSS per page. Every plugin loaded its own jQuery version.
Fix: Rebuilt on clean theme with 8 essential plugins. Migrated content from page builders to native blocks. Removed WooCommerce (4 products — replaced with Stripe payment links). Result: 52 plugins → 8, load time 11s → 1.8s, hosting $120/mo → $20/mo.
Fintech API — CTO left mid-fundraise, system undocumented, due diligence failing
Series A due diligence revealed zero test coverage, no API documentation, and a deployment process that required the former CTO's personal laptop. Investors gave 30 days to fix or deal dies.
Root cause: Single person dependency with zero knowledge transfer. Deployment tied to SSH keys on one machine. Database migrations run manually. No CI/CD. The "staging environment" was a local Docker instance on the former CTO's Mac.
Fix: Set up automated CI/CD (GitHub Actions), containerized properly, wrote API documentation from reverse-engineering, added critical-path test coverage (62% in 3 weeks), created deployment runbooks. Due diligence passed. Round closed.
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