Maturely
Weekly, plain-English security and business-logic audits for solo founders who shipped fast with an AI coding tool — scans the codebase, flags real risk in auth, payments and customer data, and hands back a fix-ready prompt.
Overview
Solo and two-person teams who shipped fast with an AI coding tool now have real users — and real risk they can't see, because nobody on the team is a security engineer and raw scanner output (hundreds of generic SAST warnings) is unreadable noise nobody will ever triage.
Maturely runs a hybrid detection pipeline — deterministic scanners plus a custom LLM lane built specifically to reason about payment and auth logic — and translates every finding into a plain-English write-up with a copy-ready fix prompt. It connects through a GitHub App and can open the fix directly as a pull request.
Features
- Weekly automated security and business-logic scans of a connected GitHub repo
- Findings organized into a prioritized action queue by area: payment, authentication, customer data, code quality
- Every finding explained in plain English — what the risk is and why it matters
- Copy-ready, scoped fix prompt per finding, written for a coding agent
- One-click "open as pull request" auto-fix flow via the GitHub App
- Weekly digest email summarizing new and resolved findings
- Security health score with week-over-week trend tracking
Technologies
- Next.js 16 (App Router), React 19
- Prisma 7 + Postgres
- NextAuth v5 — GitHub OAuth plus GitHub App install flow
- Semgrep + ESLint — static analysis layer
- Custom LLM detection lane (AI SDK: Anthropic, Google, OpenAI) — semantic payment/auth risk detection
- Vercel Sandbox — isolated, ephemeral scan execution
- Stripe — subscription billing
- Resend — weekly digest email delivery
- Vitest — three test suites covering the detection pipeline
Technical highlights
- Hybrid detection pipeline: deterministic scanners catch pattern-matchable issues, a custom LLM lane purpose-built for payment/auth semantics catches what pattern rules structurally can't — like a database rule that never checks who's asking, or a checkout route that trusts a client-supplied price
- Every scan runs inside a fresh, ephemeral Vercel Sandbox instance — isolating execution of static analysis against a third party's real repository was a hard requirement, not an afterthought
- GitHub App integration with direct auto-fix pull requests, not just a findings report
- Cron-scheduled weekly-scan and weekly-digest jobs run automatically without user action
Status
Fully built with real detection logic and a working demo dashboard — the screenshot on this page is the actual product UI, not a mockup. Not yet deployed to a public domain.