A complete picture of how Syscribe goes from PR merge to deployed documentation — and why every step matters.
Your CI/CD webhook fires immediately when a PR merges. Syscribe ingests the diff — not just which files changed, but what the changes mean semantically.
If the PR references a Linear or Jira ticket, Syscribe fetches the issue description, acceptance criteria, and context. The generation prompt knows why the code changed, not just what changed. That's what makes the documentation accurate.
Before generating anything, Syscribe consults the knowledge graph — a per-customer map of your documentation landscape built during onboarding and updated on every PR.
The graph knows which sections map to which code paths, your confirmed structure, which sections are human-owned and must not be touched, and which doc types are relevant to which repos. This is what allows surgical section-level targeting — not full-page rewrites.
For each affected section, Syscribe generates a draft calibrated to the doc type and audience. Internal docs, user-facing guides, and API references require different tone and depth — each draft reflects that.
Edit learning from previous approvals shapes each draft. After Month 3, Syscribe drafts in your team's voice — not generic AI prose.
Reviewer receives an email with a direct link. The review screen shows all doc types for this PR in a tabbed view. Each tab shows a what-changed summary, the proposed update within its section context, and a side-by-side diff against the previous deployed version.
Edit inline. Instruct the agent to revise. Or approve as-is. Each tab has its own approve/reject action. Nothing deploys until approved.
On approval, Syscribe deploys to every configured target at once. Only the specific section affected by this PR is updated — the rest of the page is untouched. Your editorial sections, how-to guides, and manually written content are never overwritten.
When a reviewer edits a draft before approving, Syscribe captures the diff. When a reviewer instructs the AI to revise, that interaction is stored too. Both become training signal for future drafts on the same customer, target, and doc type.
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