Own Technical Debt Analyzer AI.

What you'd own.

Every artifact below transfers to your accounts on day one. The whole engine.

• ●GitHub Marketplace listing with established SEO rankings and community credibility
• ●Landing page, Product Hunt launch plan, and Hacker News strategy
• ●AI analysis engine architecture and LLM integration for code scanning
• ●Financial model with unit economics and 3-tier SaaS pricing framework
• ●Customer acquisition playbook for bottom-up developer tool adoption motion
• ●Founder positioning docs targeting CTOs and engineering leads at startups

What Roll Digital builds for you.

Custom scope to take Technical Debt Analyzer AI from MVP shell to operating business.

• ▸Production FastAPI backend with Postgres for analysis data storage
• ▸Stripe billing integration with usage metering across all pricing tiers
• ▸GitHub Marketplace app certification and OAuth integration for discovery
• ▸Multi-tenant auth with email and social login sign-in methods
• ▸LLM API cost tracking with automatic per-customer throttling and alerts
• ▸Analytics dashboard showing customer usage, LLM costs, and churn signals
• ▸Customer success program including 30-day onboarding and technical support

Why this could work.

GitHub Marketplace reaches engineers directly, avoiding costly sales cycles. Free-to-paid conversion is a proven SaaS motion. The TAM of 30k growing companies that outgrow free static analysis is real. However, SonarQube and CodeClimate already own significant mindshare. To win requires demonstrating AI catches real issues those tools miss. Engineering evangelism and word-of-mouth from early reference customers could drive adoption.

What you'd risk.

Established vendors like SonarQube and CodeClimate already dominate this segment with deep customer relationships. GitHub's own code scanning, plus Copilot and Cursor adding native technical debt signals, risks commoditizing the tool before customer traction builds. LLM API costs scale directly with codebase size, destroying unit economics on large repos at lower price points. Clear AI differentiation beyond existing static analysis remains unproven.

Who this fits.

An experienced SaaS founder or dev tools operator with GitHub marketplace credibility, or a technical leader at a VC-backed developer tools startup. Must be comfortable managing LLM infrastructure costs and willing to empirically prove AI differentiation before scaling marketing spend. Best fit: an operator who can secure and nurture early reference customers to drive word-of-mouth adoption.

Timeline.

From contract signing to operating business.

1. Day 1
   Review code base, customer interviews, financials, and product roadmap
2. Week 1
   Deploy FastAPI backend, Postgres, and GitHub Marketplace app integration
3. Week 3
   Stripe billing, multi-tenant auth, and first test customer signups
4. Week 6
   Product Hunt and Hacker News launch, gather customer feedback
5. Day 90
   LLM cost tracking live, analytics dashboard active, churn metrics stable

Pricing.

Three ways in, depending on how much you want to build yourself.