Own Shipcheck.

What you'd own.

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

• ●Landing page designed for Product Hunt and Show HN launches
• ●Agent workflow spec for scanning AI-generated codebases and detecting vulnerabilities
• ●Financial model projecting $54K mid-range Year 1 ARR and margins
• ●Founder persona as Camille Forrest with indie hacker community credibility
• ●Free tier strategy and word-of-mouth growth playbook for indie developers
• ●Semgrep rule libraries tuned for AI codegen anti-patterns and security
• ●Early customer email sequences and outreach templates for initial traction

What Roll Digital builds for you.

Custom scope to take Shipcheck from MVP shell to operating business.

• ▸Production backend API in Python, Postgres database, Docker deployment ready
• ▸GitHub OAuth integration and repository scanning with permission management
• ▸Stripe billing for free, pro, and enterprise tier management
• ▸LLM-powered vulnerability detection for auth flows and environment handling
• ▸Tiered report generation with ship-blockers, warnings, and remediation guidance
• ▸Targeted rescan workflow for flagged files without full repo re-ingestion
• ▸Email notification system for scan results and deployment blocker alerts
• ▸Admin dashboard for usage metrics, customer management, and revenue tracking

Why this could work.

The GTM works. A free tier targeting 300K developers shipping with AI tools drives word-of-mouth in indie communities proven to spend $150 yearly on pre-launch tooling. SEO for 'AI app scanner' captures search intent before Product Hunt. Shipping Camille Forrest's founder story and AI-specific vulnerability detection differentiates from Semgrep and GitHub Actions. Year 1 revenue of $54K is within reach.

What you'd risk.

Vibe coders ship-and-fix rather than scan-first, so the behavior change required may not exist. Semgrep, GitHub Actions, and free linters already cover static analysis. The 'AI-specific issues' wedge is thin and hard to defend. LLM scan costs scale with repo size, compressing margins if power users with large codebases adopt first. Market adoption risk is real. The 31% success probability reflects genuine uncertainty in product-market fit.

Who this fits.

A solo SaaS founder with indie developer connections, or a product agency diversifying into tools. You need GTM experience or existing relationships in the AI development tools community. Success depends on iterating the scanning engine based on real-world edge cases and building word-of-mouth momentum among Cursor and Copilot users who are actively shipping.

Timeline.

From contract signing to operating business.

1. Day 1
   Receive all product assets, documentation, and initial customer contacts
2. Week 1
   Production backend with GitHub OAuth deployed and tested live
3. Week 3
   Stripe billing across all three pricing tiers fully operational
4. Week 6
   LLM vulnerability detection and tiered reporting engine live for users
5. Day 90
   Fifty active free tier users validating initial product-market fit

Pricing.

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