MeshOS
Stop shipping code that surprises you. MeshOS gives every application a comprehensive AI review before it reaches production — security vulnerabilities, architecture problems, test coverage gaps, performance issues, and more. What used to take senior engineers days now happens automatically, consistently, every time.
What MeshOS does for your team
Engineering teams waste enormous time on manual code reviews that miss things, take too long, and vary by reviewer. MeshOS eliminates that inconsistency. Upload any application and receive a complete, multi-dimensional analysis from 23 specialized AI agents — each focused on one discipline, none distracted by anything else.
You get:
- Complete security analysis — vulnerabilities, exposed secrets, authentication gaps, injection risks, and dependency CVEs
- Architecture assessment — pattern detection, service mesh compatibility, API design review, database schema analysis
- Code quality scoring — maintainability, dead code, error handling, duplication
- Test coverage evaluation — coverage metrics, test quality, effectiveness gaps
- Three readiness scores — Cloud, Library, and Service Mesh readiness with actionable recommendations
- Automated patch generation — fixes for actionable findings, ready to apply
The 23-agent review system
Each of MeshOS's 23 AI agents has a single, focused job. They run sequentially so later agents can build on earlier findings. The final agents synthesize everything, validate results for accuracy, and generate a ranked report with supporting evidence.
No finding appears without a file, line number, and recommendation. Every result is cross-validated against the actual source code. If an agent can't support a finding with evidence, it's removed.
The result is a review you can actually act on — not a wall of noise.
Learn more about the AI agent system →
Three readiness scores
Code that works in development often breaks in production. MeshOS gives you three objective scores that measure real production readiness, not just whether the code compiles.
Cloud Readiness measures whether your application follows cloud-native patterns: containerization, 12-factor compliance, stateless architecture, observability instrumentation, and infrastructure-as-code usage. A low score tells you exactly what to fix before deploying to AWS, Azure, or GCP.
Library Readiness measures whether a codebase is fit for promotion to a shared library: clean API surface, documentation completeness, test coverage, and semantic versioning. Use this score to manage what gets added to your internal component library.
Service Mesh Readiness measures compatibility with modern service mesh architectures: sidecar proxy support, distributed tracing, circuit breaker patterns, and observability hooks.
Learn more about readiness scoring →
Application registry
Every application you analyze lives in a central registry — versioned, searchable, and with full history. Compare any two versions to see exactly what changed, which issues were resolved, and which new ones appeared. Teams use this to track improvement over time and enforce standards before merge.
Collaboration notes, review history, approval workflows, and published domain tracking are all built in. The registry becomes your organization's shared understanding of what every application looks like.
Learn more about the application registry →
Component federation
MeshOS automatically detects reusable components across every application it analyzes — React contexts, utility functions, custom hooks, service classes, and more. Each detected component gets a reusability score and a clear signal: keep it in place, or promote it to the shared library.
Promoted components go through an approval workflow and become available across every team and application. Dependencies are tracked automatically, so you always know what depends on what.
Learn more about component federation →
How teams use MeshOS
Before shipping a major feature — upload the branch, run a targeted security and architecture review, get findings before code review starts. Senior engineers spend their time on the issues that matter, not the ones an AI can catch.
As part of CI/CD — trigger reviews automatically on every pull request. Gate merges on minimum readiness scores. Ensure compliance standards are met before anything lands in main.
Quarterly codebase audits — analyze your entire codebase portfolio, track readiness score trends, identify which teams are improving and which need support.
Before migrating to the cloud — use Cloud Readiness scores to build an honest, prioritized migration backlog. Know exactly what needs to change before starting the work.
Results
Teams using MeshOS report finding and fixing critical security vulnerabilities they had no idea existed — not once, but repeatedly, in applications that had been in production for years. Manual review cadences that used to take two days per application compress to under an hour. And because the analysis is consistent, standards actually hold across teams.
Ready to start? See the Application Registry documentation to upload your first application, or jump to AI Agents to understand what MeshOS finds and how.