An Evidence-Led Scanner for AI-Built and Modern Web Applications.
A secure software-assurance platform that grades ZIP uploads and GitHub repositories, explains every finding, and identifies AI app-builder signals with confidence and evidence.
Public and private repositories
Security, performance, scalability and AI origin
Network-off ephemeral scan containers
Files, lines, fixes and confidence bands
The challenge
AI app builders make it possible to ship software quickly, but technical teams, buyers and investors still need to understand whether a codebase is secure, maintainable and ready to scale. A useful assessment could not stop at a generic score; it needed to show the evidence behind every verdict.
The platform also had to treat every submitted repository as hostile. ZIP archives and Git histories may contain traversal attempts, archive bombs, secrets or unexpected binaries, so the scanner itself could not become the vulnerability it was meant to detect.
AI-origin detection required especially careful product language. The system can identify patterns consistent with builder platforms such as Lovable, Bolt.new, v0 or Replit, but it cannot reliably prove that a human used AI-assisted coding tools. Confidence, evidence and caveats therefore had to be part of the product model.
The approach
Safe repository ingestion
Users can submit a ZIP archive or connect a public or private GitHub repository. Source artefacts are stored privately, materialised into restricted workspaces and removed after the configured retention window.
Isolated static-analysis workers
Every scan runs without executing the submitted application inside an ephemeral, non-root Docker container with no network, a read-only filesystem, dropped capabilities and strict CPU, memory, process and time limits.
Multiple evidence engines
Semgrep, Gitleaks, Trivy, OSV Scanner and custom rule engines analyse code, dependencies, secrets, performance risks, scalability patterns and AI app-builder signals before findings are normalised and deduplicated.
Actionable reporting
Laravel orchestrates scan jobs and turns the results into graded modules, executive summaries, file-and-line evidence, suggested fixes, confidence bands, PDF exports, rescan comparisons and an anonymous leaderboard.
The results
Mission Control is best described as an AI-era software assurance and codebase pre-flight platform. It gives founders, engineering teams and technical reviewers one place to assess whether an application is safe, efficient and structurally ready for further investment.
The most important result is trust rather than a single score. Submitted code is never executed, findings are tied back to evidence, AI-origin conclusions remain appropriately caveated, and the scanner architecture keeps untrusted source away from the main application environment.
What did not go perfectly
Static analysis is a powerful pre-flight check, not a replacement for penetration testing, load testing or expert code review. AI-builder fingerprinting identifies platform signals rather than proving whether a person used AI-assisted development.
Project gallery
Screenshots and project visuals from the build, optimisation, or platform work.
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