How it works
Use the AI you prefer, then bring its output through connected Git or MCP and Shipvise-managed Git. Checks, optional review, staging, approval, production and rollback follow one controlled lifecycle wherever the application runs.
For developers
Connect GitHub or GitLab. Shipvise captures each change, moves it through the controlled release workflow and can hand the approved release to your infrastructure.
GitHub / GitLab → controlled change → review → tests → staging → approval → production
For AI builders and non-developers
Create a project, connect an MCP-compatible AI agent and let Shipvise manage repositories, branches and releases behind the scenes.
AI agent → MCP → controlled workspace → release candidate → staging → approval → production
A push from GitHub or GitLab, or an MCP agent working through Shipvise-managed Git, creates a traceable revision.
The revision is built on a controlled branch in an ephemeral workspace with no access to production, the host or other projects.
A container image is produced and identified by its digest. This exact artifact is what later runs in production.
The image is deployed to an isolated staging environment with its own URL and its own variables.
You try the application while platform checks report build status, health, exposed secrets and dependency issues.
When the change warrants more scrutiny, request AI review, automated PR tests or optional senior review. You see the estimated and maximum credit cost before confirming.
Promotion to production requires an explicit approval. An agent cannot approve on your behalf.
The exact release approved on staging goes to the production environment managed by Shipvise or connected to your own infrastructure. Runtime logs, rollback and future updates remain part of the same lifecycle.
If the release misbehaves, one click returns production to the previous verified digest. No rebuild is needed and the action stays in release history.
The release stops at build. Logs are kept, staging is untouched, and production keeps running the previous release.
Staging still runs so you can investigate, but the release is marked as blocked until the finding is resolved or explicitly accepted.
You get the findings, fix them with your AI or with help, and create a new release. The rejected release stays in the history.
Bring the project to Shipvise, verify the release on staging and move it through a controlled path to production. Keep your infrastructure or use a Shipvise-managed environment.
