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NextStepMike

Case Study

AI-Powered Quality Assurance & Monitoring Platform for web applications.

Founder
Product Manager
Developer Tools
SaaS Monetization

Role

Founder / Product Manager

Key Skills Used

Developer tooling • AI integration • SaaS monetization • QA systems design

The Problem

Developers and teams — especially solo founders and "vibe coders" — ship web applications without reliable ways to catch quality issues early. QA tools are fragmented: one tool for SEO, another for performance, another for security, another for accessibility. Most require expensive expertise to interpret. Critical failures in production often go undetected for days, costing users and revenue. There was no single, approachable platform that could run comprehensive automated testing AND tell you how to fix what it found.

The Solution

NextStepMike is a comprehensive testing and monitoring suite that combines 25+ automated tests, real-time monitoring, AI-powered fix generation, and strategic SEO intelligence into one platform. It calculates a unified health score so users know exactly where their app stands — and what to do next. From detecting a broken checkout flow to generating store submission evidence packs, it covers the full quality lifecycle.

Test Coverage

1–2 tools, manual effort

25+ automated test types

Fix Guidance

Interpret errors yourself

AI-generated code-level fixes

Detection Speed

Days or never

Minutes per scan

Product Strategy

  • All-in-one QA platform: replace the fragmented toolkit of 5–10 separate tools
  • AI as the intelligence layer — not just detection, but diagnosis and repair guidance
  • Unified health score that communicates risk in plain language, not raw metrics
  • Real browser automation (Steel.dev) for testing dynamic, JS-rendered applications
  • SEO intelligence suite powered by Perplexity API for competitive context
  • Store submission readiness as a differentiator for indie founders and agencies
  • Freemium-to-Pro funnel with usage gating enforced at the scan level
  • Dark-first, "Grandma-Simple" UX to lower the barrier for non-technical users

My Role

As founder and product lead, I:

  • Identified the market gap: developers needed one QA tool, not five
  • Designed the full testing architecture — 25+ test categories, real browser automation, health scoring
  • Built the AI layer: fix generation, AI response verification, report optimization for AI ingestion
  • Defined the product roadmap prioritizing high-impact test categories first
  • Designed the subscription monetization model with usage-based gating
  • Created the UX philosophy: "Grandma-Simple" documentation, contextual guidance, What's Next Advisor
  • Integrated Stripe billing, Supabase auth/RLS, and Perplexity AI for SEO intelligence
  • Shipped and iterated based on real developer feedback

Product Decisions

Why build all-in-one vs. a focused single-purpose tool?

Developers are already overwhelmed by context-switching between tools. A unified health score across all dimensions — not just SEO or just performance — creates a dramatically better workflow.

Why use real browser automation instead of static analysis?

Modern web apps are JavaScript-rendered SPAs. Static HTML analysis misses the majority of real issues. Real browser execution (headless Chromium via Steel.dev) is the only way to test what users actually see.

Why make reports AI-ingestion optimized?

Developers increasingly use AI coding tools to fix issues. A report that can be pasted directly into Claude or Cursor without hallucination dramatically shortens the fix cycle.

Why include store submission readiness?

Indie founders and agencies building apps for clients need evidence packs and compliance checklists for App Store and Play Store approval. No other QA tool addresses this specific need.

Screenshots & UI

NextStepMike Logo
NextStepMike QR Code

Scan to try it live

Additional UI screenshots coming soon.

What I Would Improve Next

Future improvements for NextStepMike:

  • CI/CD pipeline integration — automatically trigger scans on every deployment (GitHub Actions, Vercel)
  • Deeper visual regression with AI-generated diff explanations, not just pixel diffs
  • Expanded user flow monitoring — record and replay multi-step user journeys
  • Team collaboration features — assign issues, comment on failures, track resolution
  • Integrations with Jira and Linear for seamless bug ticket creation from scan results
  • White-label reporting for agencies to deliver branded QA reports to clients