Rescuing a Failing AI-Built Project Management App

TaskFlow's founder used AI coding tools to build and launch a project management SaaS in weeks. With 800 active users paying monthly subscriptions, the app was generating real revenue — until it started falling apart. Daily crashes, data loss, and authentication failures were driving users away at 15% per week. Two weeks from shutting down, they called us.

The Situation

TaskFlow was a project management app built almost entirely with AI coding assistants. The founder, a non-technical entrepreneur, had gone from idea to paying product in under a month — an impressive feat. But the AI-generated codebase had no tests, no error handling, and no consideration for what happens when 800 people use the software simultaneously.

What Was Breaking

• Daily crashes — unhandled errors in the real-time sync cascaded through the entire app, taking down all users when one person's session failed
• Data loss — a race condition in task assignments meant that when two team members updated the same project simultaneously, one person's changes disappeared silently
• Authentication failures — session tokens stored in localStorage with no refresh logic meant users got randomly locked out, sometimes losing work in progress
• Degrading performance — an unindexed database query scanned every task on every page load, meaning the app got exponentially slower as teams added more tasks
• Security vulnerabilities — API keys hardcoded in client-side bundles, no input sanitisation, and SQL injection vectors in the search feature

The Business Impact

Users were churning at 15% per week. Monthly recurring revenue had dropped 40% in three weeks. Support tickets were piling up faster than the founder could respond. Review sites were filling with one-star ratings mentioning crashes and lost data.

Our Approach

We didn't rewrite the app. Rewrites are expensive, risky, and would have taken months — time TaskFlow didn't have. Instead, we triaged the codebase in 48 hours and attacked the most critical failures first.

Phase 1: Emergency Stabilisation (Days 1–3)

We identified the three paths that were actively losing users and revenue:

1. Authentication — Replaced the broken localStorage token system with a proper session management flow using HTTP-only cookies and automatic refresh
2. Real-time sync — Fixed the race condition by implementing optimistic locking on task assignments, so concurrent edits merge instead of overwrite
3. Error boundaries — Added React error boundaries so that when one component fails, it fails in isolation instead of crashing the entire app

After day 3, the daily crash reports stopped.

Phase 2: Performance & Security (Days 4–7)

4. Database indexes — Added composite indexes on the three most-hit queries. The worst one dropped from 12 seconds to 40 milliseconds
5. Security hardening — Removed client-side API keys, added input sanitisation, parameterised all database queries, and rotated every exposed credential
6. Memory leaks — Found and fixed WebSocket connections that were never cleaned up, causing the server to crash every 8–12 hours

Phase 3: Guardrails & Handoff (Days 8–14)

7. Test coverage — Wrote integration tests covering every flow that had been breaking: auth, task CRUD, team sync, and permissions. Reached 85% coverage on critical paths
8. CI/CD pipeline — Set up GitHub Actions to run tests on every PR, blocking merges that break existing functionality
9. Monitoring — Added error tracking (Sentry), uptime monitoring, and alerting so the founder knows immediately if something goes wrong
10. Documentation — Wrote a runbook covering the architecture decisions, known limitations, and how to safely make common changes

Results

Metric	Before	After
Daily crashes	3–5	Zero in 30 days
Worst page load	12 seconds	Under 800ms
Weekly user churn	15%	3%
Security vulnerabilities	12 critical	Zero
Test coverage (critical paths)	0%	85%
App Store rating	2.1 stars	4.4 stars (3 months later)

The Outcome

TaskFlow not only survived — it grew. With a stable product, the founder was able to focus on marketing and feature development instead of firefighting. Six months after our rescue, TaskFlow had grown to 2,400 active users and raised a seed round.

The founder now uses our ongoing maintenance plan ($2,000/month) for security patches, performance monitoring, and small feature iterations. The app hasn't had a single unplanned downtime since our engagement.

Key Takeaway

AI coding tools are incredible for getting from zero to prototype. But prototype-quality code breaks under real-world load with real users and real money on the line. The gap between "it works on my laptop" and "it works reliably for 800 paying customers" is where we operate. You don't need a rewrite — you need an experienced team to add the engineering rigour your AI assistant left out.